economics trade creation and diversion …...trade creation, exporter diversion and importer...
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ECONOMICS
TRADE CREATION AND DIVERSION UNDER THE
THAILAND-AUSTRALIA FREE TRADE AGREEMENT (TAFTA)
by
Sally Milton Business School
University of Western Australia
and
M A B Siddique Business School
University of Western Australia
DISCUSSION PAPER 14.26
TRADE CREATION AND DIVERSION UNDER THE THAILAND-AUSTRALIA FREE
TRADE AGREEMENT (TAFTA)*
Sally Milton** and M A B Siddique**
Please address correspondence to:
Dr. M. A. B. Siddique, Associate Professor, Economics
M251, UWA Business School, The University of Western Australia
35 Stirling Highway, Crawley, WA. 6009
Phone: +61 (0)8 6488 2941
Fax: +61 (0)8 6488 1016
E-Mail: [email protected]
DISCUSSION PAPER 14.26
*The authors acknowledge the generous grant that they have received from the UWA Business School in order to facilitate the preparation of this paper. ** University of Western Australia (UWA) Business School
ABSTRACT
This paper examines the impact of the Thailand-Australia Free Trade Agreement (TAFTA)
on bilateral merchandise trade flows between Australia and Thailand. Using aggregated data,
an augmented gravity model is estimated in an attempt to quantify the trade creation and/or
diversion effects of the agreement. The model includes 178 countries and is estimated using
panel data over the period 1998 to 2012. The inclusion of three variables describing TAFTA
membership (i.e. intra-TAFFTA trade creation, exporter diversion and importer diversion)
allows for the correct identification of Vinerian trade creation and trade diversion effects. The
estimation method accounts for country heterogeneity, endogeneity and potential selection
bias through the use of time-invariant, time-varying, country-specific and country-pair
effects. Diagnostic checks indicate the presence of heteroscedasticity and serial correlation,
which are controlled for in a fixed effects model with robust standard errors. The results
indicate that the Thailand-Australia Free Trade Agreement has had modest trade creation
effects, with little evidence to suggest that this is at the expense of trade diversion. The
findings of the study have obvious policy implications.
Keywords: Free Trade Agreement; Thailand; Australia; FTA; Trade and Growth; Trade
Flow Effects; Gravity Model; Trade Policy
JEL Classification: F13, F14, F15, O24
1 Introduction
One of the emerging trends in international economics in recent years has been the
proliferation of Regional Trading Agreements (RTAs), including Free Trade Agreements
(FTAs), Preferential Trading Agreements (PTAs) and Customs Unions (CUs)1. According to
the World Trade Organisation2, as of 31 July 2013, 379 RTAs were in force with a total of
575 notified3. This global acceleration in trading agreement activity has led to debate among
international economists about the merits of entering into such agreements. While some see
bilateral liberalisation as trade creating and progression towards free world trade4 , others fear
that preferential and regional trading agreements will be a hindrance and act to divert trade
towards those with preferential treatment. Despite this, the reality is that practically all WTO
members are parties to FTAs and/or are actively involved in FTA negotiations (DFAT,
2010).
After the East Asian Financial Crisis of 1997-98, there has been a notable increase in policy
efforts aimed to hasten progress of economic integration in the region (DFAT, 2010). As a
result of this, around half of the FTAs notified to the WTO are between countries in the Asia-
Pacific region (Kohpaiboon, 2008), with Australia being an active player. Since 2003,
Australia has successfully negotiated and implemented six FTAs with: Singapore, the United
States, Chile, the Association of South East Asian Nations (ASEAN), Malaysia and Thailand
which, when considered with the Australia-New Zealand Closer Economic Relations Trade
Agreement (implemented in 1983), account for 28 per cent of Australia’s total trade 5 .
Negotiations for a further nine FTAs 6 (including five bilateral and four plurilateral
agreements) are also being undertaken by the Australian government, which account for a
further 45 per cent of Australia’s trade.
1 The cause of the expansion of FTAs in the global trading system is not so clear. The stalemate in the WTO’s Doha Round is seen by many authors as a potential explanation for the increased interest in FTAs. Given this, it is reasonable to consider that countries interested in trade liberalisation, when faced with these stalled multilateral negotiations, have sought bilateral and plurilateral liberalisation through FTAs. 2 Data includes trade in goods and services and are taken from the WTO website: http://www.wto.org/english/tratop_e/region_e/region_e.htm. Accessed 17 September 2013. 3 Since the establishment of the WTO in 1995, over 400 notifications of RTAs have been received compared to only 124 from 1948-1994. 4 The WTO Secretariat has argued that FTAs have facilitated negotiation of rules and commitments that go beyond what was possible at the time multilaterally; and that some of these rules have developed into agreements in the WTO. 5 Figure represents two way trade in goods and services from 2008-2009, taken from Department of Foreign Affairs and Trade website http://www.dfat.gov.au/fta/. Accessed 17 September 2013. 6 Bilateral: China, Japan, Korea, India and Indonesia. Plurilateral: Trans-Pacific Partnership Agreement (TPP), the Gulf Cooperation Council (GCC), the Pacific Trade and Economic Agreement (PACER plus) and the Comprehensive Economic Partnership Agreement (RCEP).
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As trade makes such a crucial contribution to Australia’s economic performance, maintaining
access to existing open markets and liberalising access to other markets through FTAs are
key trade policy objectives for the Australian government (DFAT, 2010). Mortimer and
Edwards (2008) reflect this view and state that “[t]he increasing number of global and
regional FTAs has important implications for Australia: as more FTAs are negotiated,
Australian goods and services exports face the prospect of loss of market share. Australia
needs an active FTA agenda for the future to deal with these risks but also to expand export
and investment opportunities” (pg 92).
2 Aims and Objectives
In light of this growing interest in FTAs in Australia and abroad, this paper examines the
conflicting effects of trade liberalisation, trade creation and trade diversion (hereafter referred
to as “trade flow effects”), in the context of one of Australia’s recently signed FTAs, the
Thailand- Australia Free Trade Agreement (TAFTA).
The study specifically addresses two research questions. First, has the TAFTA had a trade
creation effect and led to an increase in bilateral trade between Australia and Thailand?
Second, to the extent that the TAFTA has increased trade, how much was a result of trade
diversion? The inclusion of three dummies describing TAFTA membership (intra-TAFFTA
trade creation, exporter diversion and importer diversion) allows for the correct identification
of these Vinerian trade effects [see (Egger, 2002); (Carrère, 2006); (Martínez-Zarzoso et al.,
2009) and (Yang and Martínez-Zarzoso, 2013)]. The study of the TAFTA at this time is of
particular interest as it is Thailand’s first comprehensive FTA signed with a developed
country and as it is in its ninth year of operation, sufficient time has now passed to discern the
Agreement’s preliminary effect on bilateral trade flows between Australia and Thailand.
3 Motivation
A few studies have provided ex-ante assessments of the TAFTA, including the Centre for
International Economics (CIE, 2004), Hoa (2004) ,Siriwardana (2006) and Sally (2007).The
most comprehensive study was commissioned by the Department of Foreign Affairs and
Trade and conducted by the Centre for International Economics (CIE, 2004). They examined
the economic gains likely to arise from trade liberalisation under the TAFTA and concluded
that the TAFTA will be economically beneficial for both Australia and Thailand; however,
2
the gains for Thailand will be larger due to higher initial barriers to trade and greater relative
importance of trade with Australia to Thailand.
Hoa (2004) explores the impact of bilateral trade between Thailand and Australia on Thai
growth and draws conclusions as to the potential challenges and benefits that may arise from
the TAFTA. The results indicate that trade activities between Australia and Thailand do
affect Thailand’s growth and therefore the TAFTA can be expected to enhance bilateral trade
and improve both countries welfare.
Siriwardana (2006) provides an overview of the effects of various FTAs, including the
TAFTA, on the Australian economy using a computable general equilibrium (CGE) model.
The results predict both Australia and Thailand are to gain from the TAFTA in terms of
welfare. Additionally, Australia’s trade balance is projected to improve and Thailand’s to
deteriorate.
However, literature examining the ex-post assessment of the TAFTA is very scant. From a
policy-making perspective it is very important to ‘take stock’ of the effects of an FTA after it
is established as there are widespread policy implications (domestic and foreign) along with
implications for ongoing FTA negotiations. Comparing the expected effect and the effect
that eventuates from the FTA can also help policy makers improve pre-FTA assessment
methods. Siddique (2013) emphasises this point with reference to the TAFTA and states that
“(e)xperience from the TAFTA could be used by Australian policy makers to design and
establish future FTAs with Southeast nations which have similar economic characteristics to
Thailand” (pg 15).
The specific contributions of this paper are as follows. First, to emphasise the importance of
using proper econometric techniques in the modelling of trade flows effects of FTAs and to
use caution when making inferences from trade data. Second, to fill a gap in the literature on
trade flow effects of FTAs and provide a broad assessment of the impact of the TAFTA on
trade flows. Given the mixed predictions of these ex-ante assessments, the results of this
paper will provide useful information about the accuracy of ex-ante FTA impact studies that
go on to assist policy makers to make better informed inferences about the effects of future
FTAs.
3
4 Australia- Thailand Bilateral Trade
In 2012, Australia and Thailand celebrated 60 years of bilateral relations, of which trade is an
important factor. During 2012, Thailand was ranked as Australia’s 10th largest export partner
and Australia as Thailand’s 8th largest7, highlighting the importance of bilateral trade between
the two countries. Australia’s main exports to Thailand are primary products and
manufactured metals while manufactures dominate Thailand’s exports to Australia. In 2011-
2012, bilateral merchandise trade between Australia and Thailand was valued at around
AU$15 billion comprising of roughly AU$5 billion worth of Australian exports to Thailand,
and AU$10 billion worth of imports from Thailand.
Table 4.1 below summarises Australia’s merchandise Trade with Thailand in the years
leading up to and following the implementation of the TAFTA, disaggregated into exports
and imports.
Table 4.1: Australia's Merchandise Trade with Thailand
Year Exports (A$m)
Growth (yoy, %)
Share of Total
Exports (%)
Imports (A$m)
Growth (yoy, %)
Share of Total
Imports (%)
Total trade (A$m)
Growth (yoy, %)
1995 1737 27.0 2.4 1014 15.6 1.3 2751 22.5 2000 1959 40.0 1.8 2816 27.9 2.4 4775 31.1 2004 3059 35.9 2.6 3772 4.7 2.7 6831 16.7 2005 4131 35.0 3.0 4812 27.6 3.1 8943 31.0 2006 4281 3.6 2.6 6258 30.0 3.6 10539 17.8 2010 5855 38.0 2.5 11005 -5.4 5.2 16860 6.3 2011 6766 15.6 2.6 8441 -23.3 3.7 15207 -0.98 2012 4873 -28.0 2.0 10175 20.5 4.2 15048 -1.1
Source: Table created by authors using data from ABS Cat. 5368.0 July 2013. Notes: (i) Exports refer to Australian exports to Thailand and imports refer to Australia’s imports from Thailand (ii) Shaded cells indicated figures collected after the implementation of the TAFTA
It is apparent that bilateral trade between Thailand and Australia has grown significantly
since the early 1990s, from $AU 2.7 billion (bn) in 1995 to just over $AU 15 bn in 2012. This
growth has occurred primarily on the import side, from just over $AU 1 bn in 1995 to over
$10 bn in 2012, which is intuitive as Australia has historically been a net importer. It can be
seen from this table that there has been a marked increase in total bilateral trade between
Australia and Thailand since the TAFTA came into effect in 2005 (see Table 1). The increase
in trade has occurred primarily on the import side, with Thai imports increasing in
7 Source: Direction of Trade Statistics (DOTS)
4
importance in Australia’s trade portfolio. This conclusion is supported by Athukorala and
Kohpaiboon (2011). The seemingly inconsistent trade patterns post-TAFTA implementation
may be able to be explained by external factors, such as the 2008 global financial crisis and
flooding in Thailand during 2009 causing disruptions to trade. The following empirical
analysis hopes to disentangle these factors from the effects of the TAFTA.
Figure 4.1: Relative Share of Trade Between Australia and Thailand
Source: created by authors using data from WITS, UN COMTRADE.
It is apparent that bilateral trade between Thailand and Australia has grown significantly since the early 1990s, from $AU 2.7 billion (bn) in 1995 to just over $AU 15 bn in 2012. This growth has occurred primarily on the import side, from just over $AU 1 bn in 1995 to over $10 bn in 2012, which is intuitive as Australia has historically been a net importer. It can be seen from this table that there has been a marked increase in total bilateral trade between Australia and Thailand since the TAFTA came into effect in 2005 (see Table 1). The increase in trade has occurred primarily on the import side, with Thai imports increasing in importance in Australia’s trade portfolio. This conclusion is supported by Athukorala and Kohpaiboon (2011). The seemingly inconsistent trade patterns post-TAFTA implementation may be able to be explained by external factors, such as the 2008 global financial crisis and flooding in Thailand during 2009 causing disruptions to trade. The following empirical analysis hopes to disentangle these factors from the effects of the TAFTA.
Figure 4.1 above compares the relative importance of Australia (Thailand) in Thai (Australian) trade from 1990 to 2012 through each country’s relative trade share. It is expected that bilateral trade between Australia and Thailand has become relatively more important for each country post-TAFTA implementation. Prior to the implementation of the TAFTA (to 2004) it appears as though the relative trade shares for both Thailand and Australia were fairly similar. After the implementation of the TAFTA (2005 onwards) there is a clear divergence, with bilateral trade between Australia and Thailand becoming relatively much more important for Thailand. Interestingly it appears that, since 2005, Australia-Thailand bilateral trade has become relatively less important for Australia. As mentioned above, these trends
0
1
2
3
4
5
6
1990 1994 1998 2002 2006 2010
Rela
tive
trad
e sh
are
Relative share of trade
Aus Share of Thai Exports Thai Share of Aus Exports
5
likely incorporate external macroeconomic shocks and therefore may not be reflective of the TAFTA’s actual impact.
An annual trade intensity index (TII) for Australia and Thailand from 1990 to 2012 is shown in Error! Not a valid bookmark self-reference., which measures the importance of bilateral trade between the two countries based upon their importance in world trade or the intensification of the trade between Australia and Thailand relative to the rest of the world. It is defined as the share of one country’s exports to a partner divided by its share of world exports. An index of 1 indicates that the bilateral trade flow is as expected, given the country’s importance in world trade. It is expected that this index will increase post-TAFTA implementation as the trade between Australia and Thailand becomes relatively more important for each country relative to the rest of the world. Trade is more intensive than normal if the TII is greater than one. It is evident from the figure that this appears to be true for both Thailand and Australia; however again it appears that trade has intensified more for Thailand post TAFTA implementation and has become less intense for Australia
Figure 4.2: Trade Intensity Index for Australia and Thailand
Source: created by authors using data from WITS, UN COMTRADE.
A number of studies have examined the historical trading relationship between Australia and
Thailand, including DFAT (2010) and Siddique (2013). DFAT (2010) finds that Australian
export growth to Thailand is about 50 per cent higher on average per annum in the four years
since the implementation of the TAFTA than was the case four years prior, with growth in
the exports of manufactured products, Australia’s primary export to Thailand, notably
increasing. Siddique (2013) notes the stark increase in trade between Australia and Thailand
6
since the early 1990s, but cautions the conclusion that the TAFTA has had a large part to play
in this increase.
5 Theoretical Background
A consensus has emerged out of recent empirical work by Carrère (2006), Baier and
Bergstrand (2007), Magee (2008) and Eicher et al. (2012) that, in general, the trade creating
effects of FTAs outweighs the trade diversion effects8. This consensus forms the premise for
the development of the hypotheses examined in this paper (outlined in section 6.1).
5.1 Modelling of the Impact of FTAs on Trade Flows
As the theoretical papers on preferential trade theory suggest that FTAs may be beneficial or
harmful depending on the relative magnitudes of trade creation and diversion, empirical
studies attempting to estimate these effects are particularly relevant. Existing studies
exploring changes in trade patterns can be divided into two groups: ex-ante analyses using
historical trade patterns to calculate the predicted effect of a reduction or elimination of trade
barriers, as in CIE (2004); and ex-post analyses that examine trade flows after an FTA has
come into force, as in Carrère (2006). As the focus of this study is an ex-post assessment of
the TAFTA, ex-post studies will be the focus of the following review of the empirical
literature.
There has been little concurrence among international economists on the appropriate
methodology for examining the impact of FTAs on trade flows. Despite this, the gravity
model has gained widespread popularity and is used in this paper to estimate the magnitude
and direction of trade flow effects of the TAFTA.
5.1.1 The Gravity Model
The most successful and extensively used model in international trade research for the last 50
years has been the gravity model. This model was first applied to international trade flows by
Tinbergen (1962) and Pöyhönen (1963) and has been used for analysing the effects of FTAs
on bilateral trade flows ever since. It is based upon Newton’s Law of Gravitation and when
applied to international trade, predicts that the volume of trade between two countries in time
t, 𝑋𝑖𝑗𝑡 should increase with their incomes, 𝑌𝑖𝑡 , 𝑌𝑗𝑡 (used as proxies for market size) and
8 Ghosh and Yamarik (2004) disagree with this consensus and suggest that the trade creation effect found in the literature is overstated and does not reflect the sample information but rather the prior beliefs of the researcher.
7
decrease with the geographical distance between them, 𝐷𝐼𝑆𝑇𝑖𝑗 (used as a proxy for
transportation costs) 9,10:
𝑋𝑖𝑗𝑡 = 𝛽0𝑌𝑖𝑡𝛽1𝑌𝑗𝑡
𝛽2𝐷𝐼𝑆𝑇𝑖𝑗𝛽3𝐹𝑖𝑗𝑡
𝛽4𝑢𝑖𝑗𝑡 (1)
where 𝐹𝑖𝑗𝑡 represents any other factors influencing trade flows between countries and 𝑢𝑖𝑗𝑡 is
the error term. The multiplicative form as specified above in Equation 1 can be estimated
using generalised linear modelling (GLM)11, however it is more common to express the
gravity equation using the following log-linear specification and estimate it using ordinary
least squares (OLS):
𝐿𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝐿𝑌𝑖𝑡 + 𝛽2𝐿𝑌𝑗𝑡 + 𝛽3𝐿𝐷𝐼𝑆𝑇𝑖𝑗+𝛽4𝐹𝑖𝑗𝑡 + 𝑢𝑖𝑗𝑡 (2)
where L denotes variables in natural logs, 𝑋𝑖𝑗𝑡 are the exports from country i and j in year
t, 𝑌𝑖𝑡(𝑌𝑗𝑡) indicate the nominal gross domestic product (GDP)of country i (j) in year t, 𝐷𝐼𝑆𝑇𝑖𝑗
denotes the geographical distance between the capital cities of countries i and j, 𝐹𝑖𝑗𝑡 are a set
of dummy variables that represent any other factors that influence trade between countries i
and j , and 𝑢𝑖𝑗𝑡 is a normally-distributed error term.
To explore the effects of trade policy and other cultural and political factors, it is common
practice to include various variables and indicators within the model, such as colonial links,
adjacency, common language and dummy variables denoting FTA membership. Numerous
papers [see (Baier and Bergstrand, 2004); (Baxter and Kouparitsas, 2006); (Eicher et al.,
2012)] have found these cultural and political factors to be significant determinants of the
volume of bilateral trade and therefore they are included in the gravity model estimated
within this paper.
The gravity model is particularly well defined for ex-post assessments of trade agreements
for two main reasons. Firstly, the model seems to represent a relevant counterfactual to
isolate the effects of a FTA , that is, the model suggests a normal level of bilateral trade for
the sample so dummy variables are able to capture the effects of a FTA (Carrère, 2006).
9 Specification as found in Martínez-Zarzoso (2013), pg 318. 10 These relationships are demonstrated in Appendices A1 and A2 for the data used in the empirical section of this paper. 11 “GLM assumes that there is a function that explains the relationship between the variance and the mean and does not assume constant variance” (Martínez-Zarzoso ,2013, pg 318).
8
Secondly, one is able to isolate the trade flow effects of a FTA by controlling for the effects
of other trade determinants.
In light of the recent expansion of FTAs, an increasing number of studies have attempted to
analyse the impacts of trade policy, such as FTAs, using the gravity model; some of the more
prominent being McCallum (1995), Rose (2000), Baier and Bergstrand (2004) and Helpman
et al. (2008); with mixed results. The majority of studies have found FTAs and RTAs to be
trade creating, however a number of high profile studies, such as Ghosh and Yamarik (2004),
suggest that these conclusions may be misleading.
In addition to being well defined for assessments of trade policy, trade gravity model is also
able to consistently explain the variation in trade flows across a wide range of time periods
and countries which makes it, according to De Benedictis and Taglioni (2011) one of the
most stable empirical relationships in economics.
5.1.2 Theoretical Foundations for the Gravity Equation
The preceding discussion outlined the historical background of the gravity model, the key
studies using it for ex-post analysis of FTAs and presented some explanations as to why it is
used so frequently. A number of recent papers have provided comprehensive outlines of the
historical background, theoretical foundations and estimation issues of the gravity model [See
(Helpman et al., 2008); (De Benedictis and Taglioni, 2011); and (Head and Mayer, 2013)].
This section will explore the gravity model’s theoretical foundations in more depth.
Although the gravity model has been used extensively in the empirical literature since
Tinbergen (1962) and Pöyhönen (1963), the theoretical foundations have been built up over
many years through the contributions of many authors (Evenett and Keller, 1998).
Early trade theorists [see (Linnemann, 1966); (Leamer and Stern, 1970); (Leamer, 1974)]
provided further motivations for a gravity model analysis of trade flows. It was, however,
Anderson (1979) who was the first to present a more comprehensive theoretical foundation
for the gravity model. He concluded that the gravity equation could be derived from the
properties of expenditure systems based on goods differentiated by country of origin12, Cobb-
Douglas preferences and, in an appendix, constant elasticity of substitution (CES)
preferences.
12 The assumption that products are differentiated by country of origin is commonly referred to as the Armington assumption.
9
Theoretical work subsequent to Anderson (1979) has shown that gravity models can arise
from a range of trade theories13. Bergstrand (1985 and 1989) and Helpman and Krugman
(1985) show that a gravity model can be developed from a model of trade based on
monopolistic competition. This model is based on assumptions similar to those of Anderson:
CES preferences and product differentiation by country of origin. Deardorff (1998)
demonstrates a traditional factor-proportions explanation of trade can give rise to a gravity
model. Later work by Eaton and Kortum (2002) and Helpman et al. (2008) has shown that the
gravity model can also arise from Ricardian and Heckscher-Ohlin (HO) type models.
The contribution of recent studies regarding the theoretical foundations of the gravity model
has emphasised the importance of determining the specifications and variables used from
economic theory (UN and WTO, 2012). This allows appropriate inferences to be drawn from
estimations using the gravity model. One of the most important contributions in this regard
has been made by Anderson and Van Wincoop (2003).
Anderson and Van Wincoop (2003) show that for a gravity model to be correctly specified it
must control for relative trade costs14. The results from their study demonstrate that relative
trade costs [“Multilateral trade-resistance terms (MTRTs)”] are an important determinant of
bilateral trade and are typically not included in the standard gravity model, leading to biased
estimation. The gravity model specifications used in this paper includes country fixed effects
as a proxy for MTRTs, as in Rose and Van Wincoop (2001), Baldwin and Taglioni (2006)
and Martínez-Zarzoso (2013).
With the publication of Eaton and Kortum (2002) and Anderson and Van Wincoop (2003) ,
the traditional view of gravity equations lacking theoretical foundations was dismissed (Head
and Mayer, 2013)15. These models paved the way for estimation methods that take into
account the structure of the models. The fixed effect models used in this paper are consistent
with theory and an example of an estimation method that does not involve strong structural
assumptions on the underlying gravity model (Head and Mayer, 2013).
13 See Deardorff (1998 ), Evenett and Keller (1998) and Feenstra et al. (2001) for useful summaries of the theoretical foundations 14 The propensity of country j to import from country i is determined by country j’s trade cost towards i relative to its weighted average trade costs and to the weighted average trade costs facing exporters in country i. from Anderson and Van Wincoop (2003). 15 Head and Mayer (2013) state that “ [s]ince neither model relie[s] on imperfect competition or increasing returns, there [is] no longer a reason to believe that gravity equations should only apply to a subset of countries…”
10
Despite this, much of the gravity literature has been highly critical of the estimation methods
used for gravity equations and has hence identified many important limitations and common
modelling mistakes. These issues are discussed briefly in the following section.
5.1.3 Existing ex-post Assessments of Trade under TAFTA
Since the TAFTA came into effect in 2005, several studies have examined aspects of the
agreement, including, Chiasakul et al. (2010), DFAT (2010), Athukorala and Kohpaiboon
(2001) and Siddique (2013). The hypotheses addressed in this paper have not been
systematically examined in these existing papers; however, the existing studies do provide
some useful insights.
Chiasakul et al. (2010) studied the sector specific effects of the TAFTA on the Thai economy
with a focus on the liberalisation of services. The study uses a CGE model and finds that the
TAFTA benefits Thailand regardless of the level of liberalisation of services. This result
provides further motivation for the focus on merchandise trade as found in this paper.
DFAT (2010) provides an overview of Australia’s existing bilateral and regional trade
agreements. The report examines a number of areas including but not limited to: the
reduction of trade and investment barriers; safeguards against new barriers; FTAs role in
lending support to the WTO; global economic developments and the promotion of regional
integration and the impact of FTAs on Australia’s trade and economic performance. With
regards to the TAFTA, DFAT (2010) find that growth in two-way trade (both imports and
exports) between Australia and Thailand has increased faster post-implementation . It is
found that average Australian export growth to Thailand (12.2 per cent a year) is greater than
that to the rest of the world (11.6 per cent a year).
Athukorala and Kohpaiboon (2011) provide the most comprehensive assessment of the
TAFTA to date. They examine the impact of the TAFTA on bilateral trade between Australia
and Thailand, paying particular attention to rules of origin (RoO), sector specific effects and
the utilisation of tariff preferences. Their findings suggest that the TAFTA has contributed to
an expansion of trade between Australia and Thailand, but that this can be accounted for by
an expansion in automotive imports to Australia from Thailand.
Finally, Siddique (2013) explores the trade relationship between Australia and Thailand
between 1990 and 2011 and finds that the composition of trade has changed significantly. He
suggests that the changes in trade patterns are part of long term structural changes and not
11
necessarily related to the implementation of the TAFTA. This paper aims to add to this
existing literature by providing estimates of the trade flow effects of the TAFTA.
6 Trade Creation and Trade Diversion under the TAFTA: An Empirical Analysis
Overview
This section provides the empirical analysis of trade creation and trade diversion under the
TAFTA. Section 6.1 outlines the development of the hypotheses. Section 6.2 describes the
data, the research method and variables employed in the study. Section 6.3 presents some
initial diagnostics of the data, whilst Section 6.4 describes the model in detail. The results are
presented in Section 6.5 followed by a robustness analysis in Section 6.6.
6.1 Development of Hypotheses
The simple concepts of trade diversion and trade creation highlight two essential areas of
interest to consider as we examine the ex-post effects of TAFTA16. First, to what extent did
trade between Australia and Thailand increase as a result of trade liberalisation under the
TAFTA? Second, was any of the increase in trade between Australia and Thailand at the
expense of other trading partners, that is, a result of trade diversion?
The preliminary analysis of the bilateral trade relationship between Thailand and Australia as
presented in section 4 suggested an expansion of trade between Thailand and Australia since
the TAFTA came into force. This naturally leads to the first hypothesis, which states that:
H1: The TAFTA has led to increased bilateral merchandise trade between Australia and
Thailand
To make any inferences about the welfare effects of the TAFTA on member countries or its
impact on the global trading system one must also determine whether there has been any
significant trade diversion. As bilateral trade between Australia and Thailand accounts for a
small share of world trade and according to Athukorala and Kohpaiboon (2011), and is
confined to a limited number of sectors, it is not expected that the TAFTA has significantly
diverted trade away from non-member countries. This leads to the development of the second
hypothesis, which states that:
16 Closely modelled on research questions presented in Clausing (2001)
12
H2: The increase in merchandise trade between Australia Thailand due to TAFTA cannot be
accounted for by a reduction in TAFTA member trade with non-member countries.
Both of these hypotheses will be addressed through the addition of three dummy variables
(BOTHin, IMPORTERin and EXPORTERin), which allow us to isolate the trade creation and
trade diversion effects of TAFTA. A summary of these effects can be found in Appendix A 4.
6.2 Analysis of Data
Data Sources
This paper uses a strongly balanced panel data set consisting of annual observations for a
sample of 178 countries17 over the period 1998 to 2012 to evaluate the hypotheses outlined in
Section 6.1 (see Appendix A1 for a complete list of countries used). This length of period is
chosen to provide enough data to obtain consistent estimates while still remaining
computationally feasible, given the limited scope of this paper. The sample consists of 23891
country pairs, 285922 observations18, and is constructed by merging data aggregated at the
country level from a wide variety of sources
Following criticisms made by Baldwin and Taglioni (2006) on the use of incorrectly deflated
data, only nominal data is used in this study. Merchandise export data is taken from the
International Monetary Fund’s (IMF) Direction of Trade Statistics (DOTS) and used as a
measure of the volume of bilateral trade. Countries’ GDP and total population, obtained from
the World Bank’s World Development Indicators (WDI), are used as proxies for market size.
Data on distance, common language, adjacency and common colonial links are from Centre
d'Etudes Prospectives et d'Informations Internationales (CEPII)19. These dummy variables
are set constant during the sample period. Finally the TAFTA and AFTA dummies are
created by the authors using information obtained from the WTO’s RTA Database20. A full
list of variables can be found in Table 6.2 below. Descriptive statistics are reported in
Appendix A 5 and do not show any particularly noteworthy results.
17 As Australia and Thailand are both global traders and FTAs are part of a multilateral trading system, it is important to estimate the impact of TAFTA on all countries and not just member countries (DFAT, 2010). 18 Missing values are dropped from the sample. It is assumed, as in,Helpman et al (2008), that all non-reported information is zero. Missing values represent 39.50 % of total data. This implies there may be a selection bias. 19 Information sourced from: http://www.cepii.fr/anglaisgraph/bdd/distances.htm 20 Information sourced from : http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx
13
6.3 Initial Diagnostics
Some initial diagnostics were conducted to identify problems with the dataset before
conducting further analysis.
As the gravity model deals with observations that are potentially heterogeneous, the
assumption of homoscedasticity of the error term is likely violated. This assumption was
tested using a Wald test for group-wise heteroscedasticity in fixed effects models. The results
suggest that heteroscedasticity is present and it is for this reason that robust standard errors
have been used for all fixed effects regressions in this paper21.
As serial correlation is a common problem in macro panels (Wooldridge, 2002) a test for
autocorrelation in panel data is conducted that indicated the presence of significant serial
correlation22.
Leamer (1994) argued that it is important to look at the simple correlation matrix between
dependent and independent variables. Therefore, the bivariate relationship between the
explanatory variables is shown in Table 6.1, which displays the pair-wise correlations for all
of the explanatory variables used in the study.
There is a strong positive correlation between the level of exports and GDP with a stronger
correlation evident for GDP of the exporting country. See Appendix A 1 for a visualisation of
this relationship. There is also a positive correlation between the level of export and
population, with the relationship again being stronger on the export side. As expected, a
negative relationship exists between the level of exports and distance. See Appendix A 2 for a
visualisation of this relationship.
With respect to the variables of interest, it appears as though they are all significantly
correlated with the level of exports. It is interesting to note that the dummy for TAFTA trade
creation is not significantly correlated with the historical and cultural dummies. This is
intuitive as Thailand and Australia do not share a common language, are not contiguous and
have not shared a common coloniser.
21 H0: homoscedasticity , χ2(23891)= 5.6e+09 22 H0: no first order autocorrelation, F(1, 20665)=1686.864
14
Table 6.1: Variable Correlation Matrix
lexports lgdp_exporter lgdp_importer lpop_exporter lpop_importer ldistcap colony contig comlang_off bothin exporterin importerin afta
lexports 1
lgdp_exporter 0.5491*** 1
lgdp_importer 0.3501*** 0.0305*** 1
lpop_exporter 0.3816*** 0.7239*** 0.0021 1
lpop_importer 0.2639*** 0.0021 0.7239*** -0.0046*** 1
ldistcap -0.2279*** -0.0547*** -0.0547*** -0.0478*** -0.0478*** 1
colony 0.1411*** 0.0854 *** 0.0854*** 0.0492** 0.0492*** -0.0610
*** 1
contig 0.1586*** 0.0370*** 0.0370*** 0.0669*** 0.0669*** -0.3558
*** 0.0944 *** 1
comlang_off 0.0100*** -0.0908 *** -0.0908*** -0.0707*** -0.0707*** -0.1158
*** 0.1402 ***
0.1077*** 1
bothin 0.0142*** 0.0082*** 0.0082*** 0.0051*** 0.0051*** 0.0014 -0.0006 -0.0008 -0.0024 1
exporterin 0.0647*** 0.1805*** 0.0114*** 0.0684*** 0.0013 0.0460 ***
-0.0041 ***
-0.0035 ** -0.0012 -0.0005 1
imrporterin 0.0338*** 0.0114*** 0.1085*** 0.0013 0.0684*** 0.0460 ***
-0.0041 ***
-0.0035
** -0.0012 -0.0005 -0.0006 1
afta 0.0564*** 0.0187*** 0.0187*** 0.0316*** 0.0316*** -0.1008
*** -0.0057
*** 0.0926*** -0.0113*** -0.0003 0.0332*** 0.0332*** 1
Source: Created by Authors Notes: (i) The symbols *, ** and *** denote statistical significance at the 10%, 5% and 1% levels respectively for a two tailed t-test. (ii) Shaded squares are indicative of a relationship with no significant correlation. (iii) Refer Table 4.2 for definitions of the variables.
15
6.4 The Model
Following the methodology proposed by recent literature providing ex-post assessments of
trade agreements [(Clausing, 2001);(Martínez-Zarzoso and Nowak-Lehmann, 2003); and
(Carrère, 2006)] and using panel data this paper estimates a series of gravity equations using
several different models. Data is aggregated at the country level and the gravity equation is
augmented with three dummy variables denoting TAFTA membership. The three TAFTA
dummy variables follow the Vinerian specification of trade flow effects and represent trade
creation, export diversion and import diversion effects. The inclusion of these dummies
allows us to test the hypotheses outlined in section 6.1.
Unbiased and consistent estimates are obtained by including country-specific fixed effects
[see (Rose and Van Wincoop, 2001) and (Baldwin and Taglioni, 2006)] which are used as
proxies for the multilateral resistance terms (MRTs) proposed by Anderson and Van
Wincoop (2003). Time effects and country-pair effects are also included to control for
macroeconomic shocks to the global trading system and bias caused by country
heterogeneity, respectively. Further to this, the analysis considers differing impacts of time-
invariant and time-varying individual characteristics, as used by Baier and Bergstrand (2007).
Fixed effects models are used as they do not impose strong structural assumptions and are
consistent with general gravity theory (Head and Mayer, 2013).
6.4.1 Baseline Model Specification
The baseline augmented gravity equation used in this analysis is specified as follows:
𝑙𝑛𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑌𝑗𝑡 + 𝛽3𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛽4𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝛽5ln (𝐷𝐼𝑆𝑇𝑐𝑎𝑝𝑖𝑗) +
𝜃1𝐵𝑂𝑇𝐻𝑖𝑛𝑖𝑗𝑡 + 𝜃2𝐸𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝜃3𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝛽6𝐴𝐹𝑇𝐴𝑖𝑗𝑡 + 𝛽7𝐶𝑜𝑛𝑡𝑖𝑔𝑖𝑗 +
𝛽8𝐶𝑜𝑚𝑐𝑜𝑙𝑖𝑗 + 𝛽9𝐶𝑜𝑚𝑙𝑎𝑛𝑔_𝑜𝑓𝑓𝑖𝑗 + 𝑢𝑖𝑗𝑡 (3)
where 𝑙𝑛 denotes variables in natural logs, all variables are defined as in Table 6.2 below and
𝑢𝑖𝑗𝑡 is assumed to be a log-normally distributed error term.
In line with the literature and the intuition that with a high level of income the exporting
(importing) country will attract a high level of exports (imports), 𝛽1 and 𝛽2 are expected to be
16
positive. The impact of population on bilateral trade is ambiguous 23 .The coefficient for
distance, 𝛽5, is expected to be negative as it is being used as a proxy for trade costs. If both
countries i and j are members of the ASEAN-FTA they are expected to trade more with each
other, it is for this reason that 𝛽6, the coefficient for AFTA, is expected to be positive.
The dummy variables Contig, Comcol and Commlang_off represent factors that may explain
the volume of bilateral trade between two countries. Intuitively, one would expect countries
that share a common border and have common colonial links and official language to have
higher volumes of trade and it is for this reason that 𝛽7, 𝛽8 and 𝛽9 are expected to be positive.
Under this specification BOTHin, EXPORTERin and IMPORTERin are dummy variables that
measure the trade flow effects of TAFTA. A positive and statistically significant 𝜃1 is
suggestive of trade creation and indicates that bilateral trade between Australia and Thailand
is higher than normal trade levels. 𝜃2 (𝜃3) captures TAFTA member imports (exports) from
(to) the rest of the world and, depending on its sign and magnitude, may indicate trade
diversion. A summary of the effects can be found in Appendix A 4.
23 Larger populations may imply large domestic markets with less reliance on international trade, see Yang and Martínez-Zarzoso (2013) page 17, but also may be used as a proxy for the capital endowment ratio, see Carrère (2006) page 226
17
Table 6.2: Reference Table for all Variables
Variable Name Exp. Description Data Source Xijt Value of Merchandise Exports NA Value of merchandise exports from country i to j at time t: Current USD, F.O.B IMF, DOTS GDPi(j)t Nominal Gross Domestic Product + Nominal gross domestic product of country i (j) at time t: Current USD World Bank, WDI 2012 Distcapij Geodesic Distance - Great circle distance24 between capital cities of countries i and j: Kilometres CEPII POPi(j)t Population +/- Total population of country i (j) at time t World Bank, WDI 2012 Contigij Contiguity + Dummy variable, 1 if countries share a common border, 0 otherwise CEPII Commlang_offij Official Common Language + Dummy variable, 1 if countries have a an official language in common, 0 otherwise CEPII Colonyij Common Coloniser + Dummy variable, 1 if countries have ever shared a colonial link25, zero otherwise CEPII IMPORTERinijt Importer in TAFTA + Dummy variable, 1 if only importing country is a member of 0 otherwise Created by authors EXPORTERinijt Exporter in TAFTA + Dummy variable, 1 if only exporting country is a member of TAFTA, 0 otherwise Created by authors BOTHinijt Both in TAFTA + Dummy variable, 1 if both countries are members of TAFTA, 0 otherwise. Created by authors AFTAij Both in ASEAN-FTA +/- Dummy variable, 1 if both countries are members of the ASEAN-FTA, 0 otherwise Created by authors
𝐼𝑖𝑗 Country Pair Effects NA Dummy variable, 1 for all observations of trade between i and j, 0 otherwise Fixed effects estimator 𝐼𝑖(𝑗) Time Invariant Country Effects NA Dummy variable, 1 if country is i (j), 0 otherwise Created by authors 𝐼𝑡 Time Effects NA Dummy variable, 1 if year is t, 0 otherwise Created by authors 𝐼𝑖𝑡(𝑗𝑡) Time Varying Country Effects NA Dummy variable, 1 if country is i(j) at time t, 0 otherwise Created by authors Source: Created by authors Notes: (i) The Variable column displays the syntax used to represent the variables, the Name column displays the name of the variables, the Description column describes the variables, the Expected Sign column (Exp.) displays the expected sign of the associated coefficient, and the Data Source column displays the source of the data. (ii) Note: CEPII variable definitions obtained from: http://www.cepii.fr/anglaisgraph/workpap/pdf/2011/wp2011-25.pdf
24 Great circle distance defined as the shortest distance measured on the earth’s surface. 25 Colonial link is defined as a relationship between two countries in which one has governed the other for some period of time and has contributed to the current state of its institutions.
18
6.4.2 Analytical Specifications and Method
The baseline gravity model, Equation (3), is first estimated using pooled OLS to provide a
benchmark for the following analytical specifications. It is expected that the signs of the
explanatory variable coefficients will match the expectations as described above in 6.4.1. As
this specification does not consider heterogeneity caused by country and time-specific effects
the coefficients will be biased and inconsistent.
To partially correct for the bias caused by these individual differences, Equation (3) is
augmented to include time and country-specific effects [as in (Baldwin and Taglioni, 2006)]
as proxies for time-invariant MRTs , Equation (4) and is then estimated using pooled OLS.
𝑙𝑛𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑌𝑗𝑡 + 𝛽3𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛽4𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝛽5ln (𝐷𝐼𝑆𝑇𝑐𝑎𝑝𝑖𝑗
+ 𝜃1𝐵𝑂𝑇𝐻𝑖𝑛𝑖𝑗𝑡 + 𝜃2𝐸𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝜃3𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝛽6𝐴𝐹𝑇𝐴𝑖𝑗𝑡
+ 𝛽7𝐶𝑜𝑛𝑡𝑖𝑔𝑖𝑗 + 𝛽8𝐶𝑜𝑚𝑐𝑜𝑙𝑖𝑗 + 𝛽9𝐶𝑜𝑚𝑙𝑎𝑛𝑔_𝑜𝑓𝑓𝑖𝑗 + 𝐼𝑖(𝑗) + 𝑢𝑖𝑗𝑡 (4)
where 𝐼𝑖(𝑗) is a time invariant dummy variable used to capture all country-specific
characteristics and control for a country’s overall level of exports. The coefficient estimates
from this regression are expected to be more accurate than those obtained from Equation (3).
As the use of panel data allows us to control for country-pair heterogeneity through country-
pair fixed effects, subsequent specifications use fixed effect models (FEM)26 to obtain more
accurate estimations.
The third model estimated, Equation (5), is a FEM with only country-pair fixed effects27.
(Kepaptsoglou et al., 2010)
𝑙𝑛𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑌𝑗𝑡 + 𝛽3𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛽4𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝜃1𝐵𝑂𝑇𝐻𝑖𝑛𝑖𝑗𝑡
+ 𝜃2𝐸𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝜃3𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝛽6𝐴𝐹𝑇𝐴𝑖𝑗𝑡 + 𝐼𝑖𝑗 + 𝑢𝑖𝑗𝑡 (5)
where 𝐼𝑖𝑗 denotes country-pair fixed effects. These fixed effects are used to control for
country-pair heterogeneity. Note that the impact of time-invariant bilateral determinants, such
as distance, colony, common language and adjacency, are unable to be estimated using a
FEM due to perfect collinearity and they are therefore left out of the equation.
26 According to Kepaptsoglou et al. (2010), the FEM tends to provide more stable results and has been preferred in most studies. 27 Note: the country-pair fixed effect is equivalent to the classic fixed effects model estimator.
19
According to Anderson and Van Wincoop (2003), Equation (5) is incorrectly specified due to
the omission of multilateral trade-resistance terms (MRTs). In order to incorporate MRTs
into this analysis, country specific importer and exporter effects are used, as in (Baldwin and
Taglioni, 2006). The fourth model to be estimated, Equation (6), builds upon the previous
specification and includes a time dummy variable and exporter and importer effects.
𝑙𝑛𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑌𝑗𝑡 + 𝛽3𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛽4𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝜃1𝐵𝑂𝑇𝐻𝑖𝑛𝑖𝑗𝑡
+ 𝜃2𝐸𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝜃3𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝛽6𝐴𝐹𝑇𝐴𝑖𝑗𝑡 + 𝐼𝑖𝑗 + 𝐼𝑡
+ 𝐼𝑖(𝑗) + 𝑢𝑖𝑗𝑡 (6)
where 𝐼𝑡 is a time dummy variable used to eliminate bias caused by aggregate shocks to
world trade, eg. 2008 global financial crisis, and the time-invariant country specific effects,
as used in Equation (4) ,are used to control for all time invariant country characteristics.
A joint F test was conducted to determine whether time fixed effects were needed when
running further FEM. The results indicated that time fixed effects should be included in any
further analysis28.
Baier and Bergstrand (2007) note, however, that multilateral resistance is likely to be time-
varying. The fifth model to be estimated, Equation (7), therefore includes time-varying
country specific fixed effects to control for the time-varying MRTs.
𝑙𝑛𝑋𝑖𝑗𝑡 = 𝛼0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑌𝑗𝑡 + 𝛽3𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛽4𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝜃1𝐵𝑂𝑇𝐻𝑖𝑛𝑖𝑗𝑡
+ 𝜃2𝐸𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝜃3𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑖𝑛𝑖𝑗𝑡 + 𝛽6𝐴𝐹𝑇𝐴𝑖𝑗𝑡 + 𝐼𝑖𝑗 + 𝐼𝑡
+ 𝐼𝑖𝑡 + 𝐼𝑗𝑡 + 𝑢𝑖𝑗𝑡 (7)
where 𝐼𝑖𝑡 and 𝐼𝑗𝑡 are importer and exporter time varying individual effects. Note that these
effects make the estimation of country-specific variables, such as GDP, impossible due to
perfect collinearity (UN and WTO, 2012).
Equation (7) follows a specification outlined in UN and WTO (2012) which pairs time-
varying country specific effects, country-pair effects and time dummies. Interestingly, some
of the empirical literature seems to favour specifications omitting the time dummies.
To discern the impact of macroeconomic shocks on the magnitude of TAFTA’s dummy
variables, a final model is estimated, Equation (7), with the time dummy omitted.
28 H0: all year dummies jointly =0, F(14, 23890)=18.40
20
6.5 Empirical Results
The gravity equations specified above in Section 6.5 are estimated to ascertain the trade flow
effects of the TAFTA. The variables of interest are BOTHin, IMPORTERin and
EXPORTERin. The regression results are presented below in Table 6.3 which displays the
regression results with the log value of exports as the dependent variable .The variables of
interest are EXPORTERin, IMPORTERin and BOTHin..
Columns (I) and (II) of Table 6.3 show the regression results using pooled OLS to estimate
Equation (3) and Equation (4), respectively. The R-squared improves with the inclusion of
fixed effects in Column (II), suggesting that this specification describes the data more
accurately. The signs of the estimated coefficients match prior expectations and the
coefficients of our variables of interest are all positive and significant at the one-percent
level, with the exception of IMPORTERin in Column (II). These results are suggestive of
significant pure trade creation in terms of both exports and imports.
From the previous discussion, we know, however, that the coefficients of both of these
regressions are biased due to the omission of country-pair fixed effects. This bias will more
severe in column (I) where no effort is made to control for unobserved country specific
effects. The coefficients presented in column (II), although less severely biased, will still not
account for the time-invariant nature of multilateral resistance terms. The difference between
the coefficients in Column (I) and (II) indicates the severity of bias caused by exclusion of
country fixed effects and emphasises the importance of including multilateral resistance
terms in gravity analysis. It is interesting to also note that the coefficients for AFTA and
lpop_exporter switch signs.
Column (III) displays the results for a model including country-pair fixed effects, as in
Equation (5). This specification used country-pair fixed effects to mitigate the bias generated
by heterogeneity. The coefficient for intra-TAFTA trade (BOTHin) remains positive and
statistically significant; however, the coefficients for the exporter and importer effects of
TAFTA are no longer significant. This suggests that TAFTA has created trade between
Australia and Thailand and had no significant effects on both countries’ trade with the rest of
the world. Note that the AFTA coefficient is no longer significant.
As discussed in section 6.4.2 above, the use of a fixed effects model prevents the estimation
of coefficient of time-invariant variables (eg. lDistcap) due to perfect collinearity. A
21
Hausman Chi-Squared test was conducted to determine whether fixed or random effects are
more suitable. The results suggest that a fixed effect model is most suitable and rejects the
use of a random effect model 29. The results for the random effect model are shown in
Appendix A 6.
Column (IV) displays the results from a FEM with time, country-specific and country- pair
fixed effects, Equation (6). These effects are used to control for macroeconomic shocks,
country-pair heterogeneity and endogeneity caused by time-invariant MRTs. Compared with
Column (III), the coefficients of our variables of interest remain positive, decrease in
magnitude slightly and in the case of BOTHin., remain statistically significant. The value of
R-squared increases slightly from 14.1% to 14.2 %. The similar results suggest that there is
not much bias caused by time-invariant MRTs.
The results for the model, Equation (7), including time-varying fixed effects for the exporter
and importer, are presented in Column (V). As defined in Table 6.2 above, the variables of
interest vary in three dimensions: importer; exporter; and time. Baier and Bergstrand (2007)
recommend that models include exporter and importer time-varying effects to control for all
determinants of trade that vary in those dimensions. The results, presented in Column (V),
provide unbiased estimates for BOTHin, IMPORTERin and EXPORTERin. The coefficient
for BOTHin remains positive and statistically significant (at the 10% level), which suggests
that the TAFTA has caused bilateral trade between Australia and Thailand to increase. The
average treatment effect is 30.6% 30 higher than expected from normal levels of trade. The
coefficients for IMPORTERin and EXPORTERin are not found to be statistically significant,
which suggests that TAFTA has had no significant effects on Australia and Thailand’s trade
with the rest of the world. Additionally, the R-squared is improved from the preceding FEMs
estimated.
Finally, Column (VI) displays the regression results for the modified Equation (7) with the
time dummy omitted. The results when compared with the previous column are quite
shocking. The coefficients of the variables of interest are now all statistically significant (at
the 1% level) and the magnitudes of each effect are notably larger with comparable standard
errors. These results support the findings of a test conducted earlier, which determined that
time effects should be included in our analysis. As our panel data set covers a period in which
29 H0: RE model preferred, Hausman’s χ2(8)= 7042.21 30 Calculated: Average treatment effect= (exp(0.267)-1)*100)
22
there have been significant macroeconomic shocks, e.g. the 2008 global financial crisis, the
disparate results are not surprising.
In conclusion, using annual data over a 14 year period extending from 1998 to 2012, the
results suggest that trade liberalisation under TAFTA has resulted in modest trade creation
with no evidence found to support the view that this is at the expense of trade diversion.
23
Table 6.3: Gravity Model Regression Results
(I)
Pooled OLS (II)
OLS with FE (III)
FE (1) (IV)
FE (2) (V)
FE (3) (VI)
FE (4) Variable Exp Coeff. R.S.E Coeff. R.S.E.
Coeff. R.S.E. Coeff. R.S.E. Coeff. R.S.E. Coeff. R.S.E.
Constant +/- -24.26*** -0.0946 1.634 -1.161 -16.33*** -1.36 -14.29*** -1.838 14.39 - 14.30*** -0.0392 lgdp_importer + 0.817*** -0.00291 0.580*** -0.0207 0.719*** -0.0188 0.675*** -0.0221 lgdp_exporter + 1.190*** -0.003 0.331*** -0.0239 0.464*** -0.0211 0.418*** -0.028 lpop_importer +/- 0.0599*** -0.00356 0.369*** -0.0635 0.376*** -0.0717 0.384*** -0.0738 lpop_exporter +/- 0.00815** -0.00376 -0.406*** -0.0725 -0.234*** -0.0809 -0.228*** -0.0836
ldistcap - -1.326*** -0.00586 -1.706*** -0.00689 colony + 1.109*** -0.0262 1.065*** -0.0273
comlang_off + 0.976*** -0.0135 0.833*** -0.0142
contig + 0.970*** -0.0273 0.727*** -0.0284
afta +/- 1.381*** -0.0699 -0.698*** -0.0615 -0.386 -0.273 -0.351 -0.273
BOTHin + 3.178*** -0.142 1.118*** -0.0741 0.394*** -0.0517 0.387*** -0.0517 0.267* 0.145 0.693*** -0.144
EXPORTERin + 0.843*** -0.0388 0.128*** -0.0411 0.039 -0.0413 0.0314 -0.0415 0.0377 -
0.0361 0.272*** -0.0345
IMPORTERin + 0.472*** -0.0494 0.123* -0.066 0.121 -0.0753 0.114 -0.0756 0.0765 -
0.0777 0.311*** -0.0769 Observations 271,109 271,109 271,109 271,109 285,922 285,922
Number of pairid - - 23,891 23,891 24, 663 24,663 R-squared 0.623 0.724 0.141 0.142 0.189 0.185
𝐼𝑡 NO YES NO YES YES NO 𝐼𝑖𝑡(𝑗𝑡) NO NO NO NO YES YES 𝐼𝑖(𝑗) NO YES NO YES NO NO 𝐼𝑖𝑗 NO NO YES YES YES YES
Source: Created by Authors Notes: (i)‘Exp.’ refers to the expected sign of the coefficient, FE stands for fixed effect(s) and R.S.E. stands for robust standard error (ii) The estimates for the fixed effects are omitted due to space considerations; however it is indicated where they are included. (iii) The symbols *, ** and *** denote statistical significance at the 10%, 5% and 1% levels respectively. (iv). Variable definitions are provided in Table 4.2
24
6.6 Robustness and Sensitivity Analysis
Sensitivity
As Ghosh and Yamarik (2004) suggest that coefficient estimates from gravity equations are
highly sensitive to model specification, a sensitivity analysis was conducted to determine
how the results are affected when one or more of the variables are omitted from the
regression. Using existing extreme bounds analysis literature as a guide, a systematic series
of fixed effects regressions are run to assess the sensitivity of the estimated coefficients of all
non-core (testing) variables. The results from this analysis are shown below in Table 6.5.The
core variables are those required to specify the baseline gravity model and the testing
variables are all other variables used in the study.
As expected, both exporter and importer GDP are highly robust in a basic fixed effects
model. It is also found that both exporter and importer population are significant in all
possible iterations. Of our variables of interest, BOTHin is significant in all iterations and
IMPORTERin and EXPORTERin are not. Due to the parsimonious nature of the
specifications examined these results come as no surprise.
Econometric Concerns
An additional econometric concern not dealt with in earlier chapters is the presence of zero
trade flows in the data. The sample had a potential of 472590 observations, however missing
values are reported are for 39.5% of observations resulting in 285922 useable observations. If
these missing values truly indicate zero trade flows, dropping them from the sample will
result in a loss of information and estimations will produce inconsistent results (UN and
WTO, 2012)31. As in (Brun et al., 2005), zeroes are replaced with Xijt=1 and the total number
of observations is increased by 65.3%. The results are reported below in Table 6.4 which
displays the regression results with the log value of exports as the dependent variable. The
variables of interest are EXPORTERin, IMPORTERin and BOTHin.
31 See section Error! Reference source not found. for a more in depth discussion on the issue of zero trade flows.
25
When compared to the regression results in Table 6.3, it is evident that dropping zero trade
values from the data may have caused the results to be biased. The results are fairly similar
when comparing the core gravity variables; however, the TAFTA variables are significantly
different, with changes in signs. A test for selection bias would ideally be performed at this
stage; however this is outside the limited scope of this paper.
Based on the robustness results discussed in this chapter, it is evident that strong conclusions
are not able to be drawn from this analysis and the findings of trade creation under TAFTA
should be taken with a grain of salt.
Table 6.4:Regression Results for Robustness Study Addressing Zero Trade Flows
(I)
FE(1) (II)
FE(2) (III)
FE (3) Variable Exp Coeff. R.S.E Coeff. R.S.E Coeff. R.S.E Constant +/- -35.95*** -2.654 -6.034* 3.597 8.989*** -0.14
lgdp_importer + 1.186*** -0.0359 0.921*** 0.0436 lgdp_exporter + 0.583*** -0.0361 0.321*** 0.0455 lpop_importer +/- 0.294* -0.158 -0.278* 0.166 lpop_exporter +/- -0.0478 -0.152 -0.619*** 0.156
Afta +/- 1.126 -0.968 0.830 0.975 BOTHin + -0.0764* -0.0463 -0.171*** 0.0469 -0.267 -0.207
EXPORTERin + -0.276*** -0.0744 -0.345*** 0.0741 -0.119* -0.0646 IMPORTERin + 0.571*** -0.152 0.501*** 0.151 0.222 -0.162
Observations 389,297 389, 297 415,726 Number of pairid 26,998 26,998 27,878
R-squared 0.072 0.075 0.136 𝐼𝑡 NO YES YES
𝐼𝑖𝑡(𝑗𝑡) NO NO YES 𝐼𝑖(𝑗) NO YES NO 𝐼𝑖𝑗 YES YES YES
Source: Created by Authors Notes: (i) ‘Exp.’ refers to the expected sign of the coefficient, FE stands for fixed effect(s) and R.S.E. stands for robust standard error (ii) The estimates for the fixed effects are omitted due to space considerations; however it is indicated where they are included. (iii) The small constant is added to trade flows before taking logarithms. (iv) The symbols *, ** and *** denote statistical significance at the 10%, 5% and 1% levels respectively. (v) Variable definitions are provided in Table 6.2
26
Table 6.5: Summary Statistics from Sensitivity Analysis
Core variables Max Min Mean
AvgSTD PercSig Perc+ Perc- AvgT Obs
lgdp_importer
0.757873058
0.713912904
0.735769602
0.01911323 1 1 0
38.56126297 512
lgdp_exporter
0.473798782
0.441425771
0.45814902
0.021531766 1 1 0
21.27982676 512
Testing –variables Max Min Mean
AvgSTD PercSig Perc+ Perc- AvgT Obs
Bothin 0.398996
651 0.382327
408 0.390581
174 0.05953
8736 1 1 0 6.58994
9086 256 Importerin
0.12117976
0.106226355
0.113541038
0.078915569 0 1 0
1.438778721 256
Exporterin
0.048461806
0.037254144
0.043057884
0.043350366 0 1 0
0.993155321 256
lpop_exporter
-0.207481
399
-0.235163
51
-0.221228
751 0.08430
7993 1 0 1 2.62348
4716 256 lpop_importer
0.376438707
0.356366873
0.36633356
0.074836333 1 1 0
4.894833118 256
Afta
-0.370708
168
-0.393347
561
-0.381827
43 0.28462
7472 0 0 1 1.34150
9711 256 Source: Created by Authors Notes: (i) Max, Min and Mean are respectively the maximum, minimum and mean value of the coefficients over all regressions. AvgSTD and AvgT are averages of the standard deviations and t-values, respectively. PercSig gives share of regressions where the coefficient was significant. Perc+ indicates the number of times the coefficient had a positive sign and analogously for Perc-. (ii) The statistics are obtained using robust standard errors in a model with fixed country-pair effects and time-invariant effects have been omitted.
7 Summary and Policy Implications
The aim of this study was to determine whether the TAFTA has had an impact on bilateral
merchandise trade between Australia and Thailand, through trade creation, and to examine
what extent of this was a result of trade diversion. It is a widely held view that FTAs are trade
creating, yet model uncertainty and econometric issues surrounding the gravity model have
led to mixed results in studies examining the ex-post trade flow effects of FTAs
This paper assesses two hypotheses. Firstly, that the TAFTA has led to an increase in
bilateral merchandise trade between Australia and Thailand. Secondly, that this increase in
bilateral trade has not been the result of trade diversion away from TAFTA non-member
countries. To assess these hypotheses, data aggregated at the country level, including 178
countries and covering the period from 1998 to 2012, was used to estimate a series of
augmented gravity equations.
27
The endogeneity problem commonly found in gravity analyses is addressed through the use
of time-invariant and time-varying country-specific effects as suggested by Baldwin and
Taglioni (2006), Baier and Bergstrand (2007) and Martínez-Zarzoso et al. (2009). The
inclusion of these fixed effects means that unbiased estimates of the trade flow effects of the
TAFTA could be obtained. Additionally, the use of panel data allows for the mitigation of
bias generated by heterogeneity across countries through the inclusion of country-pair fixed
effects (UN and WTO, 2012).
According to the estimated results, the TAFTA has resulted in modest trade creation (at the
10% level) with no significant trade diversion effects detected. These results come as no great
surprise. As trade liberalisation under TAFTA is still occurring, it is unlikely that the full
extent of the trade flow effects of TAFTA will be realised until this process is complete32.
DFAT (2010) indicate that there is likely to be a lag between the entry into force of the
TAFTA and the realisation of its larger economic impacts. The robustness analysis conducted
suggested that these results should be viewed with caution due to potential selection bias
caused by dropping zero trade flows from the data.
These results have several implications. Firstly, the findings reiterate the importance of
controlling for bias caused by heterogeneity and endogeneity in gravity models. The analysis
shows that the use of standard OLS estimation of gravity equations likely leads to severe bias
and misleading results. Time-invariant and time-varying country effects should be included to
capture all country-specific characteristics and provide a proxy for the multilateral resistance
terms proposed by Anderson and Van Wincoop (2003). Fixed time effects should also be
included to capture any macroeconomic shocks to the global economic environment that may
influence international trade.
Based on the finding that TAFTA has created trade, the results add to the existing literature
and widespread belief suggesting that FTAs are, in general, trade creating. In light of this,
trade liberalisation under TAFTA can be considered an effective addition to the growing
number of bilateral and plurilateral agreements in the South East Asian region.
32According to CIE (2004) , TAFTA is predicted to have a larger economic impact in Thailand due to their higher initial trade barriers.
28
7.1 Limitations and suggestions for future research
There are several limitations to the findings presented in this study. Firstly, the study only
examined the impact of the TAFTA on merchandise trade and did not consider the impact on
services trade. It is important to note that the amount of bilateral trade in services between
Australia and Thailand is not insignificant and therefore further assessments of the TAFTA’s
impact on trade should attempt to incorporate services trade in the analysis.
A second limitation of this study emanates from the use of aggregated trade data. As the
removal of trade barriers is often not equal between sectors, it is feasible to presume that
trade liberalisation under the TAFTA will have had a differing impact on different sectors.
Clausing (2001) notes that the use of aggregate data may mask changes that are occurring at a
disaggregate level and several existing studies [See Chiasakul et al. (2010); Kohpaiboon
(2008); Athukorala and Kohpaiboon (2011); and Siddique (2013)] of TAFTA have suggested
that this may be the case with increases in trade confined to a limited number of sectors.
A third limitation of this study stems from the use of the gravity model. As the gravity model
has been applied in such a large number of empirical studies, the model’s limitations and
econometric modelling issues have been well documented and explored in depth by many
authors33. These issues include endogeneity bias34, selection bias due to model uncertainty35
omitted variable bias36, bias caused by heterogenous trading relationships37, the prevalence of
zero values in trade flow data38, incorrect deflation of trade flows39, the use of averaged trade
flows40, and the omission of non-tariff barrier variables41. This paper takes these issues into
consideration and uses panel data, includes time-invariant country specific effects as proxies
for MRTs, country-pair effects to control for bilateral heterogeneity and assumes that all non-
reported information reflects zero trade flows.
33 Baldwin and Taglioni (2006), De Benedictis and Taglioni (2011), UN an WTO (2012) and Head and Mayer (2013) provide useful summaries of the gravity equation’s estimation issues. 34 See Ghosh and Yamarik (2004); UN and WTO (2012); Hausman and Taylor (1981); and Baier and Bergstrand (2004, 2007, 2009) 35 See Ghosh and Yamarik (2004) 36 See Anderson and Van Wincoop (2003); and UN and WTO (2012) 37 See Cheng and Wall (1999); Egger (2000); Egger and Pfaffermayr (2003); and Baier and Bergstrand (2004) 38 See UN and WTO (2012) 39 Nominal trade flows are used in this paper. See Baldwin and Taglioni (2006) or Benedictis and Taglioni (2011) for a discussion about conversion issues. 40 Unidirectional export flows are used as the dependent variable in this paper. See Baldwin and Taglioni (2006) or Benedictis and Taglioni (2011) for a discussion about typology issues. 41 Novy (2013) raises the problem that traditional gravity analysis omit many trade cost components, such as non-tariff barriers, as it is difficult to find empirical proxies for them
29
A fourth limitation of this study is that trade liberalisation under the TAFTA is still being
implemented. The longer phase in period for Thailand means that the full extent of the
TAFTA’s impact on trade flows will not be able to be estimated until 2025 at the earliest,
when Thailand’s tariffs on Australian imports are scheduled to be eliminated. DFAT (2010)
note this in a review of Australia’s bilateral and regional trade agreements and state that due
to the staged implementation of trade liberalisation under TAFTA too few years may have
passed to effectively assess the effects of the agreement. It would be beneficial for a similar
analysis to this paper to be conducted at a later date to capture more completely the impact of
the TAFTA.
Finally, it is important to note that FTAs have effects beyond trade creation and diversion,
that is they are not just about improving market access but also aim to deal with non-tariff
barriers beyond the border. It is evident that the TAFTA goes beyond the removal of tariffs
on merchandise trade and aims to deal with non-tariff barriers such as customs procedures,
electronic commerce, competition policy, intellectual property and government procurement.
Future studies should, hence, attempt to incorporate the liberalisation of non-tariff barriers.
30
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33
Appendix A 1: Scatter Plot and Line of Best Fit for Exports vs Combined GDP(Exporter*Importer)
Source: Created by authors
A 2 Scatter Plot and Line of Best Fit for Exports vs Distance
Source: Created by authors
34
A 3: List of Countries and Corresponding Country Codes
Country code
Country Name
ABW Aruba AFG Afghanistan, Islamic Republic of AGO Angola ALB Albania ARE United Arab Emirates ARG Argentina ARM Armenia, Republic of AUS Australia AUT Austria AZE Azerbaijan, Republic of BDI Burundi BEN Benin BFA Burkina Faso BGD Bangladesh BGR Bulgaria BHR Bahrain, Kingdom of BHS Bahamas, The BIH Bosnia and Herzegovina BLR Belarus BLX Belgium-Luxembourg BLZ Belize BMU Bermuda BOL Bolivia BRA Brazil BRB Barbados BRN Brunei Darussalam CAF Central African Republic CAN Canada CHE Switzerland CHL Chile CHN China, P.R.: Mainland CIV Cote d'Ivoire CMR Cameroon COG Congo, Republic of COL Colombia COM Comoros CPV Cape Verde CRI Costa Rica CUB Cuba CYP Cyprus CZE Czech Republic DEU Germany DJI Djibouti DMA Dominica DNK Denmark DOM Dominican Republic DZA Algeria ECU Ecuador EGY Egypt ESP Spain EST Estonia ETH Ethiopia FIN Finland FJI Fiji
FRA France FRO Faroe Islands GAB Gabon GBR United Kingdom GEO Georgia GHA Ghana GIN Guinea GMB Gambia, The GNB Guinea-Bissau GNQ Equatorial Guinea GRC Greece GRD Grenada GRL Greenland GTM Guatemala GUY Guyana HKG China, P.R.: Hong Kong HND Honduras HRV Croatia HTI Haiti HUN Hungary IDN Indonesia IND India IRL Ireland IRN Iran, Islamic Republic of IRQ Iraq ISL Iceland ISR Israel ITA Italy JAM Jamaica JOR Jordan JPN Japan KAZ Kazakhstan KEN Kenya KGZ Kyrgyz Republic KHM Cambodia KNA St. Kitts and Nevis KOR Korea, Republic of KWT Kuwait LAO Lao People's Democratic Republic LBN Lebanon LBR Liberia LBY Libya LCA St. Lucia LKA Sri Lanka LTU Lithuania LVA Latvia MAC China, P.R.: Macao MAR Morocco MDA Moldova MDG Madagascar MDV Maldives MEX Mexico MKD Macedonia, FYR MLI Mali MLT Malta MMR Myanmar
35
MNG Mongolia MOZ Mozambique MRT Mauritania MUS Mauritius MWI Malawi MYS Malaysia NCL French Territories: New Caledonia NER Niger NGA Nigeria NIC Nicaragua NLD Netherlands NOR Norway NPL Nepal NZL New Zealand OMN Oman PAK Pakistan PAN Panama PER Peru PHL Philippines PNG Papua New Guinea POL Poland PRK Korea, Democratic People's Rep. of PRT Portugal PRY Paraguay QAT Qatar ROM Romania RUS Russian Federation RWA Rwanda SAU Saudi Arabia SDN Sudan SEN Senegal SGP Singapore SLB Solomon Islands SLE Sierra Leone SLV El Salvador
SOM Somalia STP Sao Tome and Principe SUR Suriname SVK Slovak Republic SVN Slovenia SWE Sweden SYC Seychelles SYR Syrian Arab Republic TCD Chad TGO Togo THA Thailand TJK Tajikistan TKM Turkmenistan TON Tonga TTO Trinidad and Tobago TUN Tunisia TUR Turkey TZA Tanzania UGA Uganda UKR Ukraine URY Uruguay USA United States UZB Uzbekistan VCT St. Vincent and the Grenadines VEN Venezuela, Republica Bolivariana de VNM Vietnam VUT Vanuatu WSM Samoa YEM Yemen, Republic of ZAF South Africa ZAR Congo, Democratic Republic of ZMB Zambia ZWE Zimbabwe
36
A 4 :Possible Trade Flow Effects of TAFTA
Exporter effects Importer effects 𝜃2>0 𝜃2<0 𝜃3>0 𝜃3<0
Intra TAFTA trade
𝜃1>0 Pure TC(X) TC+XD(𝜃1>𝜃2) or XD (𝜃1<𝜃2)
Pure TC(M) TC+MD(𝜃1>𝜃3) or MD(𝜃1<𝜃3)
𝜃1<0 XE XD + XC ME MD+MC Source: Table 1, Yang and Martínez-Zarzoso (2013) Notes: (i) θ_1 is the coefficient of BOTHin which denotes intra-TAFTA trade. θ_2 is the coefficient of EXPORTERin which denotes exports from member countries to non-member countries. θ_3 is the coefficient of IMPORTERin which denotes imports from member countries to non-member countries. (ii) TC(X) and TC(M) denote trade creation in terms of exports and imports, respectively. XD and MD denote trade diversion in terms of exports and imports, respectively. XE and ME denote expansion of extra-TAFTA exports and imports, respectively. XC and MC denote contraction of intra-FTA exports and imports, respectively.
A 5: Descriptive Statistics for Explanatory Variables
This table reports descriptive statistics for the explanatory variables. Refer to Table 6.2 for variable definitions.
Variable Obs.
Mean
S. D.
Min.
Max. lGDP 472590 23.7757 2.321659 18.15228 30.38371 lDISTCAP 472590 8.734909 0.7745998 2.349362 9.898699 lPOP 472590 15.66363 2.017288 10.70079 21.02389 CONTIG 472590 0.0171713 0.1298485 0 1 COMLANG_OFF 472590 0.141973 0.3489546 0 1 COLONY 472590 0.0111725 0.1048055 0 1 BOTHin 472590 0.0000339 0.0058185 0 1 EXPORTERin 472590 0.0059587 0.0769621 0 1 IMPORTERin 472590 0.0059587 0.0769621 0 1 AFTA 472590 .0028185 .0530148 0 1 Source: Created by Authors Notes: (i) ’Obs.’ refers to the number of observations and ‘S.D.’ refers to the standard deviation. (ii) As the descriptive statistics for both lGDP(lPOP)_exporter and lGDP(lPOP)_importer are identical, only one is reported, lGDP (lPOP)
37
A 6: Regression Results for Random Effects Model
This table shows the regression results for the random effects model as used in the Hausman Chi squared test in section 6.5. The log value of exports as the dependent variable and variable definitions are provided in Table 6.2
Variable Exp Coeff. R.S.E. Constant +/- -23.03*** -0.326
lgdp_importer + 0.804*** -0.00975 lgdp_exporter + 1.033*** -0.0111 lpop_importer +/- 0.0796*** -0.0111 lpop_exporter +/- 0.146*** -0.012
ldistcap - -1.361*** -0.0191 colony + 1.476*** -0.0921
comlang_off + 1.004*** -0.0428 contig + 1.015*** -0.0961 afta +/- 0.933*** -0.236
BOTHin + 0.576*** -0.0915 EXPORTERin + 0.127*** -0.0425 IMPORTERin + 0.147** -0.0744 Observations 271,109
Number of pairid 23,891 R-squared
Source: Created by Authors Notes: (i) ‘Exp.’ refers to the expected sign of the coefficient, FE stands for fixed effect(s) and R.S.E. stands for robust standard error. (ii). The symbols *, ** and *** denote statistical significance at the 10%, 5% and 1% levels respectively.
38
Editor, UWA Economics Discussion Papers: Ernst Juerg Weber Business School – Economics University of Western Australia 35 Sterling Hwy Crawley WA 6009 Australia Email: [email protected] The Economics Discussion Papers are available at: 1980 – 2002: http://ecompapers.biz.uwa.edu.au/paper/PDF%20of%20Discussion%20Papers/ Since 2001: http://ideas.repec.org/s/uwa/wpaper1.html Since 2004: http://www.business.uwa.edu.au/school/disciplines/economics
ECONOMICS DISCUSSION PAPERS 2012
DP NUMBER AUTHORS TITLE
12.01 Clements, K.W., Gao, G., and Simpson, T.
DISPARITIES IN INCOMES AND PRICES INTERNATIONALLY
12.02 Tyers, R. THE RISE AND ROBUSTNESS OF ECONOMIC FREEDOM IN CHINA
12.03 Golley, J. and Tyers, R. DEMOGRAPHIC DIVIDENDS, DEPENDENCIES AND ECONOMIC GROWTH IN CHINA AND INDIA
12.04 Tyers, R. LOOKING INWARD FOR GROWTH
12.05 Knight, K. and McLure, M. THE ELUSIVE ARTHUR PIGOU
12.06 McLure, M. ONE HUNDRED YEARS FROM TODAY: A. C. PIGOU’S WEALTH AND WELFARE
12.07 Khuu, A. and Weber, E.J. HOW AUSTRALIAN FARMERS DEAL WITH RISK
12.08 Chen, M. and Clements, K.W. PATTERNS IN WORLD METALS PRICES
12.09 Clements, K.W. UWA ECONOMICS HONOURS
12.10 Golley, J. and Tyers, R. CHINA’S GENDER IMBALANCE AND ITS ECONOMIC PERFORMANCE
12.11 Weber, E.J. AUSTRALIAN FISCAL POLICY IN THE AFTERMATH OF THE GLOBAL FINANCIAL CRISIS
12.12 Hartley, P.R. and Medlock III, K.B. CHANGES IN THE OPERATIONAL EFFICIENCY OF NATIONAL OIL COMPANIES
12.13 Li, L. HOW MUCH ARE RESOURCE PROJECTS WORTH? A CAPITAL MARKET PERSPECTIVE
12.14 Chen, A. and Groenewold, N. THE REGIONAL ECONOMIC EFFECTS OF A REDUCTION IN CARBON EMISSIONS AND AN EVALUATION OF OFFSETTING POLICIES IN CHINA
12.15 Collins, J., Baer, B. and Weber, E.J. SEXUAL SELECTION, CONSPICUOUS CONSUMPTION AND ECONOMIC GROWTH
39
ECONOMICS DISCUSSION PAPERS 2012
DP NUMBER AUTHORS TITLE
12.16 Wu, Y. TRENDS AND PROSPECTS IN CHINA’S R&D SECTOR
12.17 Cheong, T.S. and Wu, Y. INTRA-PROVINCIAL INEQUALITY IN CHINA: AN ANALYSIS OF COUNTY-LEVEL DATA
12.18 Cheong, T.S. THE PATTERNS OF REGIONAL INEQUALITY IN CHINA
12.19 Wu, Y. ELECTRICITY MARKET INTEGRATION: GLOBAL TRENDS AND IMPLICATIONS FOR THE EAS REGION
12.20 Knight, K. EXEGESIS OF DIGITAL TEXT FROM THE HISTORY OF ECONOMIC THOUGHT: A COMPARATIVE EXPLORATORY TEST
12.21 Chatterjee, I. COSTLY REPORTING, EX-POST MONITORING, AND COMMERCIAL PIRACY: A GAME THEORETIC ANALYSIS
12.22 Pen, S.E. QUALITY-CONSTANT ILLICIT DRUG PRICES
12.23 Cheong, T.S. and Wu, Y. REGIONAL DISPARITY, TRANSITIONAL DYNAMICS AND CONVERGENCE IN CHINA
12.24 Ezzati, P. FINANCIAL MARKETS INTEGRATION OF IRAN WITHIN THE MIDDLE EAST AND WITH THE REST OF THE WORLD
12.25 Kwan, F., Wu, Y. and Zhuo, S. RE-EXAMINATION OF THE SURPLUS AGRICULTURAL LABOUR IN CHINA
12.26 Wu, Y. R&D BEHAVIOUR IN CHINESE FIRMS
12.27 Tang, S.H.K. and Yung, L.C.W. MAIDS OR MENTORS? THE EFFECTS OF LIVE-IN FOREIGN DOMESTIC WORKERS ON SCHOOL CHILDREN’S EDUCATIONAL ACHIEVEMENT IN HONG KONG
12.28 Groenewold, N. AUSTRALIA AND THE GFC: SAVED BY ASTUTE FISCAL POLICY?
40
ECONOMICS DISCUSSION PAPERS 2013
DP NUMBER AUTHORS TITLE
13.01 Chen, M., Clements, K.W. and Gao, G.
THREE FACTS ABOUT WORLD METAL PRICES
13.02 Collins, J. and Richards, O. EVOLUTION, FERTILITY AND THE AGEING POPULATION
13.03 Clements, K., Genberg, H., Harberger, A., Lothian, J., Mundell, R., Sonnenschein, H. and Tolley, G.
LARRY SJAASTAD, 1934-2012
13.04 Robitaille, M.C. and Chatterjee, I. MOTHERS-IN-LAW AND SON PREFERENCE IN INDIA
13.05 Clements, K.W. and Izan, I.H.Y. REPORT ON THE 25TH PHD CONFERENCE IN ECONOMICS AND BUSINESS
13.06 Walker, A. and Tyers, R. QUANTIFYING AUSTRALIA’S “THREE SPEED” BOOM
13.07 Yu, F. and Wu, Y. PATENT EXAMINATION AND DISGUISED PROTECTION
13.08 Yu, F. and Wu, Y. PATENT CITATIONS AND KNOWLEDGE SPILLOVERS: AN ANALYSIS OF CHINESE PATENTS REGISTER IN THE US
13.09 Chatterjee, I. and Saha, B. BARGAINING DELEGATION IN MONOPOLY
13.10 Cheong, T.S. and Wu, Y. GLOBALIZATION AND REGIONAL INEQUALITY IN CHINA
13.11 Cheong, T.S. and Wu, Y. INEQUALITY AND CRIME RATES IN CHINA
13.12 Robertson, P.E. and Ye, L. ON THE EXISTENCE OF A MIDDLE INCOME TRAP
13.13 Robertson, P.E. THE GLOBAL IMPACT OF CHINA’S GROWTH
13.14 Hanaki, N., Jacquemet, N., Luchini, S., and Zylbersztejn, A.
BOUNDED RATIONALITY AND STRATEGIC UNCERTAINTY IN A SIMPLE DOMINANCE SOLVABLE GAME
13.15 Okatch, Z., Siddique, A. and Rammohan, A.
DETERMINANTS OF INCOME INEQUALITY IN BOTSWANA
13.16 Clements, K.W. and Gao, G. A MULTI-MARKET APPROACH TO MEASURING THE CYCLE
13.17 Chatterjee, I. and Ray, R. THE ROLE OF INSTITUTIONS IN THE INCIDENCE OF CRIME AND CORRUPTION
13.18 Fu, D. and Wu, Y. EXPORT SURVIVAL PATTERN AND DETERMINANTS OF CHINESE MANUFACTURING FIRMS
13.19 Shi, X., Wu, Y. and Zhao, D. KNOWLEDGE INTENSIVE BUSINESS SERVICES AND THEIR IMPACT ON INNOVATION IN CHINA
13.20 Tyers, R., Zhang, Y. and Cheong, T.S.
CHINA’S SAVING AND GLOBAL ECONOMIC PERFORMANCE
13.21 Collins, J., Baer, B. and Weber, E.J. POPULATION, TECHNOLOGICAL PROGRESS AND THE EVOLUTION OF INNOVATIVE POTENTIAL
13.22 Hartley, P.R. THE FUTURE OF LONG-TERM LNG CONTRACTS
13.23 Tyers, R. A SIMPLE MODEL TO STUDY GLOBAL MACROECONOMIC INTERDEPENDENCE
41
ECONOMICS DISCUSSION PAPERS 2013
DP NUMBER AUTHORS TITLE
13.24 McLure, M. REFLECTIONS ON THE QUANTITY THEORY: PIGOU IN 1917 AND PARETO IN 1920-21
13.25 Chen, A. and Groenewold, N. REGIONAL EFFECTS OF AN EMISSIONS-REDUCTION POLICY IN CHINA: THE IMPORTANCE OF THE GOVERNMENT FINANCING METHOD
13.26 Siddique, M.A.B. TRADE RELATIONS BETWEEN AUSTRALIA AND THAILAND: 1990 TO 2011
13.27 Li, B. and Zhang, J. GOVERNMENT DEBT IN AN INTERGENERATIONAL MODEL OF ECONOMIC GROWTH, ENDOGENOUS FERTILITY, AND ELASTIC LABOR WITH AN APPLICATION TO JAPAN
13.28 Robitaille, M. and Chatterjee, I. SEX-SELECTIVE ABORTIONS AND INFANT MORTALITY IN INDIA: THE ROLE OF PARENTS’ STATED SON PREFERENCE
13.29 Ezzati, P. ANALYSIS OF VOLATILITY SPILLOVER EFFECTS: TWO-STAGE PROCEDURE BASED ON A MODIFIED GARCH-M
13.30 Robertson, P. E. DOES A FREE MARKET ECONOMY MAKE AUSTRALIA MORE OR LESS SECURE IN A GLOBALISED WORLD?
13.31 Das, S., Ghate, C. and Robertson, P. E.
REMOTENESS AND UNBALANCED GROWTH: UNDERSTANDING DIVERGENCE ACROSS INDIAN DISTRICTS
13.32 Robertson, P.E. and Sin, A. MEASURING HARD POWER: CHINA’S ECONOMIC GROWTH AND MILITARY CAPACITY
13.33 Wu, Y. TRENDS AND PROSPECTS FOR THE RENEWABLE ENERGY SECTOR IN THE EAS REGION
13.34 Yang, S., Zhao, D., Wu, Y. and Fan, J.
REGIONAL VARIATION IN CARBON EMISSION AND ITS DRIVING FORCES IN CHINA: AN INDEX DECOMPOSITION ANALYSIS
42
ECONOMICS DISCUSSION PAPERS 2014
DP NUMBER AUTHORS TITLE
14.01 Boediono, Vice President of the Republic of Indonesia
THE CHALLENGES OF POLICY MAKING IN A YOUNG DEMOCRACY: THE CASE OF INDONESIA (52ND SHANN MEMORIAL LECTURE, 2013)
14.02 Metaxas, P.E. and Weber, E.J. AN AUSTRALIAN CONTRIBUTION TO INTERNATIONAL TRADE THEORY: THE DEPENDENT ECONOMY MODEL
14.03 Fan, J., Zhao, D., Wu, Y. and Wei, J. CARBON PRICING AND ELECTRICITY MARKET REFORMS IN CHINA
14.04 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART I: THE HISTORICAL CONTEXT
14.05 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART II: ‘TRANSITIONAL’ AND ‘ONGOING’ ISSUES
14.06 King, J.E. and McLure, M. HISTORY OF THE CONCEPT OF VALUE
14.07 Williams, A. A GLOBAL INDEX OF INFORMATION AND POLITICAL TRANSPARENCY
14.08 Knight, K. A.C. PIGOU’S THE THEORY OF UNEMPLOYMENT AND ITS CORRIGENDA: THE LETTERS OF MAURICE ALLEN, ARTHUR L. BOWLEY, RICHARD KAHN AND DENNIS ROBERTSON
14.09
Cheong, T.S. and Wu, Y. THE IMPACTS OF STRUCTURAL RANSFORMATION AND INDUSTRIAL UPGRADING ON REGIONAL INEQUALITY IN CHINA
14.10 Chowdhury, M.H., Dewan, M.N.A., Quaddus, M., Naude, M. and Siddique, A.
GENDER EQUALITY AND SUSTAINABLE DEVELOPMENT WITH A FOCUS ON THE COASTAL FISHING COMMUNITY OF BANGLADESH
14.11 Bon, J. UWA DISCUSSION PAPERS IN ECONOMICS: THE FIRST 750
14.12 Finlay, K. and Magnusson, L.M. BOOTSTRAP METHODS FOR INFERENCE WITH CLUSTER-SAMPLE IV MODELS
14.13 Chen, A. and Groenewold, N. THE EFFECTS OF MACROECONOMIC SHOCKS ON THE DISTRIBUTION OF PROVINCIAL OUTPUT IN CHINA: ESTIMATES FROM A RESTRICTED VAR MODEL
14.14 Hartley, P.R. and Medlock III, K.B. THE VALLEY OF DEATH FOR NEW ENERGY TECHNOLOGIES
14.15 Hartley, P.R., Medlock III, K.B., Temzelides, T. and Zhang, X.
LOCAL EMPLOYMENT IMPACT FROM COMPETING ENERGY SOURCES: SHALE GAS VERSUS WIND GENERATION IN TEXAS
14.16 Tyers, R. and Zhang, Y. SHORT RUN EFFECTS OF THE ECONOMIC REFORM AGENDA
14.17 Clements, K.W., Si, J. and Simpson, T. UNDERSTANDING NEW RESOURCE PROJECTS
14.18 Tyers, R. SERVICE OLIGOPOLIES AND AUSTRALIA’S ECONOMY-WIDE PERFORMANCE
43
ECONOMICS DISCUSSION PAPERS 2014
DP NUMBER AUTHORS TITLE
14.19 Tyers, R. and Zhang, Y. REAL EXCHANGE RATE DETERMINATION AND THE CHINA PUZZLE
14.20 Ingram, S.R. COMMODITY PRICE CHANGES ARE CONCENTRATED AT THE END OF THE CYCLE
14.21 Cheong, T.S. and Wu, Y. CHINA'S INDUSTRIAL OUTPUT: A COUNTY-LEVEL STUDY USING A NEW FRAMEWORK OF DISTRIBUTION DYNAMICS ANALYSIS
14.22 Siddique, M.A.B., Wibowo, H. and Wu, Y.
FISCAL DECENTRALISATION AND INEQUALITY IN INDONESIA: 1999-2008
14.23 Tyers, R. ASYMMETRY IN BOOM-BUST SHOCKS: AUSTRALIAN PERFORMANCE WITH OLIGOPOLY
14.24 Arora, V., Tyers, R. and Zhang, Y. RECONSTRUCTING THE SAVINGS GLUT: THE GLOBAL IMPLICATIONS OF ASIAN EXCESS SAVING
14.25 Tyers, R. INTERNATIONAL EFFECTS OF CHINA’S RISE AND TRANSITION: NEOCLASSICAL AND KEYNESIAN PERSPECTIVES
14.26 Milton, S. and Siddique, M.A.B. TRADE CREATION AND DIVERSION UNDER THE THAILAND-AUSTRALIA FREE TRADE AGREEMENT (TAFTA)
14.27 Clements, K.W. and Li, L. VALUING RESOURCE INVESTMENTS
44