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University of New South Wales
School of Economics
Honours Thesis
An Empirical Analysis of Disaggregated Australian ServiceExports
Author:
William Weatherburn
Student ID: 3379847
Supervisor:
A/Prof Glenn Otto
Dr. Pei-Cheng Yu
Bachelor of Economics (Economics) (Honours)
and
Bachelor of Science (Psychology)
31st October, 2017
Declaration
I declare that this thesis is my own work and that, to the best of my knowledge, it
contains no material that has been published or written by another person except
where due acknowledgement has been made. This thesis has not been submitted for
award of any other degree or diploma at the University of New South Wales or at
any other educational institution.
.....................................
Bill Weatherburn
31st October, 2017
i
Acknowledgements
I will be ever grateful to Glenn Otto for your assistance in developing this thesis topic
and for the considerable help throughout the year. I am particularly appreciative
of the time taken to explain and interpret various econometric techniques. A big
thank you to P.C Yu for your thesis feedback, referee reports and the general day
to day hilarity of your one liners.
Thanks also to Tess Stafford for your extensive and considerate advice on all aspects
of the thesis development. However, I think it is my grammar that has most
improved from your teaching.
An immense thanks to my family and Jordan for supporting and encouraging me
throughout my entire time at university.
Finally, I would not have survived Honours, yet alone enjoyed my time, if it were
not for my fellow classmates. Thanks to Helena for her amazing social planning,
Beatrix for commiserating with me over Micro theory, Sarah for arvo tea times,
Isabel for scrutinising all of the world’s geo-political problems, Binal for pleasantly
wasting away hours discussing East Africa, Barath for the inexorable dry jokes, Jun
for being Jun and Simon for the coffee breaks, the precision but mostly for the
insight into Sydney’s hipster scene.
ii
Contents
Declaration i
Acknowledgements ii
Table of Contents iii
Abstract vii
1 Introduction 1
2 Literature Review 4
2.1 Export Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Service Exports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Disaggregating Services . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Disaggregate Australian Services . . . . . . . . . . . . . . . . . . . . 9
3 Approach and Variables 11
3.1 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.1 Gravity Model Variables and Data Sources . . . . . . . . . . . 11
3.1.2 Time Series Variables and Data Sources . . . . . . . . . . . . 13
3.1.3 The Use of Nominal Data . . . . . . . . . . . . . . . . . . . . 15
4 The Gravity Model 16
4.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Cointegration 21
5.1 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Test for Cointegration . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6 Autoregressive Distributed Lag Model 26
6.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.1.1 Nominal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.1.2 Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
iii
6.2 Inference using Dynamic Ordinary Least Squares . . . . . . . . . . . 30
7 Error Correction Model 32
7.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
7.2 Nominal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
7.3 Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
7.4 Asymmetric Exchange Rates . . . . . . . . . . . . . . . . . . . . . . . 36
7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
8 Conclusion 39
A Gravity Model 43
B Cointegration 44
C ARDL 46
D ECM 47
iv
List of Tables
4.1 Gravity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.1 ADF and KPSS Tests . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.2 Engle-Granger Tests . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.1 Long Run Elasticities Nominal ARDL Model . . . . . . . . . . 28
6.2 Long Run Elasticities Real ARDL Model . . . . . . . . . . . . 29
7.1 Nominal Error Correction Model . . . . . . . . . . . . . . . . . . 33
7.2 Real Error Correction Model . . . . . . . . . . . . . . . . . . . . 35
7.3 ECM with Asymmetric Exchange Rate Effects . . . . . . . . . 37
A.1 Gravity Model Using Random Effects Estimation . . . . . . . 43
B.1 ADF Tests (Real Data) . . . . . . . . . . . . . . . . . . . . . . . . 44
B.2 Engle-Granger Tests (Real Data) . . . . . . . . . . . . . . . . . . 44
B.3 ADF Tests on the First Difference of the Series (Nominal
Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
B.4 ADF Tests on the First Difference of the Series (Real Data) 45
C.1 Dynamic Ordinary Least Squares Results . . . . . . . . . . . . 46
D.1 Complete Error Correction Model (Nominal Data) . . . . . . 47
D.2 Complete Error Correction Model with Trend (Nominal Data) 48
D.3 Complete Error Correction Model (Real Data) . . . . . . . . . 49
D.4 Error Correction Model for Government Exports (Real Data) 49
D.5 Complete Error Correction Model with Trend (Real Data) . 50
D.6 Error Correction Model with Asymmetric Exchange Rate
Effects (Nominal Data) . . . . . . . . . . . . . . . . . . . . . . . . 50
D.7 Error Correction Model with Asymmetric Exchange Rate
Effects (Real Data) . . . . . . . . . . . . . . . . . . . . . . . . . . 51
v
List of Figures
3.1 Composition of Australian Service Exports . . . . . . . . . . . . . . . 14
4.1 Relationship Between Aggregate Service Exports and Distance . . . . 19
5.1 Australia’s Service Exports . . . . . . . . . . . . . . . . . . . . . . . . 22
5.2 Cointegrating Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.1 Service Exports (Real Data) . . . . . . . . . . . . . . . . . . . . . . . 30
7.1 Fitted, Actual and Residual Plots . . . . . . . . . . . . . . . . . . . . . 34
D.1 Fitted, Actual and Residual Plots of all Error Correction Models . . . . . 52
vi
An Empirical Analysis of Disaggregated
Australian Service Exports
William Weatherburn
Abstract
I empirically model the demand for Australian service exports. Rather than consider
service exports in the aggregate, I disaggregate service exports into nine categories.
Using three export data sets and a variety of econometric techniques, I find that
there are vast differences in how service exports are affected by economic growth,
the exchange rate, the distance between Australia and its trading partners and
whether the trading partner speaks English as an official language. I find that
education exports are the most sensitive to economic growth but the least affected
by fluctuations in the exchange rate. I also find that, despite the absence of
transportation costs, all service exports are negatively affected by distance. Finally,
I find mixed evidence for cointegration between individual service exports, the
exchange rate and trading partner GDP. It is evident that examining services in
the aggregate masks how sensitive individual categories of services are to standard
trade variables.
vii
Chapter 1
Introduction
Over the 40 year period from 1977 to 2017, the value of Australia’s service exports
rose from $0.93 billion to $71 billion.1 In 2016, service exports exceeded agricultural
exports by over 60% and three quarters of the Australian population was employed
in the service sector.2 Despite the importance of services to the Australian economy,
there have been few empirical studies of Australia’s service exports. Most studies are
restricted to large, multi-country analyses that assume goods and service exports are
interchangeable (Senhadji and Montenegro, 1999). If service exports are examined,
they are almost always considered in the aggregate (Ceglowski, 2006; Kouparitsas,
Luo, Smith, et al., 2017). The purpose of this paper is to add to the sparse literature
on Australia’s disaggregate service export industry. I divide services into nine
categories and estimate the long-run elasticities of income demand as well as the
sensitivity of these exports to the exchange rate. I also examine whether factors
that determine the trade in goods, such as sharing a common language and the
distance between trading partners, also determine the export of Australian services.
I ask whether the aggregation of service exports masks how responsive the individual
categories are to these variables.
The definition of a service is an activity that is intangible and does not result in
ownership upon sale. A doctor diagnosing a patient is a service, as is guiding tourists
up the Sydney Harbour Bridge. A service export is simply a service that is bought
by foreigners. It includes obvious activities such as tourism but also more complex
activities such as the selling of financial products to other countries. The paucity of
research into Australian services can be partly explained by the difficulty involved
in measuring the value as well as the volume of these activities.
In Australia, services have long been viewed as a means of increasing Australia’s
export revenue. For instance, one argument for the deregulation of the financial
sector in the 1980’s was that Australia could develop into the financial hub of
Asia. The interest in promoting Australia’s service exports faded at the turn of
the century as China’s economic development stimulated a demand for commodities
1Nominal data obtained from Australian Bureau of Statistics Catalogue 536801.2Data obtained from Australian Bureau of Statistics Catalogue 536801.
1
which saw a 345% rise in the price of coal and an 820% rise in the price in iron
ore in the space of ten years.3 As the price of these commodities has now fallen,
the push to encourage Australian service exports is back at the forefront of policy
discussion and is likely to remain so. The long-term prospects of coal as an export
are looking increasingly grim. Concerns about climate change and clean air, as
well as the growing investment in gas-fired power stations has meant that coal will
not play as significant a role in electrical generation as it has done in the past.
Commentators as varied as British Petroleum (BP) and the Grantham Institute (an
environmental think tank) predict that the world demand for coal could peak in as
little as eight years. In response, the Australian government has singled out tourism,
health and finance as services that could fill this possible revenue void (Productivity
Commission, 2015).
Unfortunately, there has been little empirical analysis of how Australian services
are affected by standard trade variables such as foreign income, prices, the exchange
rate and the distance between Australia and its trading partners. Do education
and financial services rise by the same amount when the world economy grows at
one percent? How sensitive to the exchange rate are transport services? And is
Australia’s distance from its trading partners a severe hindrance to the trade in
services? These are all important questions if Australia wishes to transition away
from being a commodity-exporting country.
To answer these questions, I use a variety of econometric techniques that have
been used extensively in the trade literature. Using a simple Gravity Model I find
that trading partner GDP, sharing a common language and the distance between
Australia and its neighbours all have a statistically significant effect on Australia’s
service exports. Distance has a strong impact on all Australia’s service exports
but the effect differs greatly between the exports. For instance, a 1% increase in
the distance between Australia and a trading partner reduces telecommunication
exports by 0.7% but insurance and pension services by 3%.
I then formulate export demand equations as a function of trading partner income
and the exchange rate; a specification that is consistent with Cheng (2016) and
Senhadji and Montenegro (1999). As both exports and income trend upward, there
is a concern that normal ordinary least squares techniques would lead to a spurious
regression. Export demand equations are therefore estimated using a cointegration
framework. Augmented Dickey Fuller tests clearly indicate that the exchange rate,
3Calculated as the simple percentage change in price from June 2003 to June 2013. Data fromIndex Mundi.
2
GDP and all categories of exports are non-stationary variables. However, the results
of cointegration tests lead to contradictory conclusions. A bounds testing procedure
(Pesaran, Shin, and Smith, 2001) indicates that service exports are cointegrated
with income and the exchange rate. However, Engle-Granger tests do not support
this conclusion. Under the assumption that the variables are actually cointegrated,
an error correction model is estimated to examine the short run dynamics of service
export demand. All service categories are found to respond quickly to deviations
from the long-run equilibrium. For example, other business services correct by 25%
each year if there is a deviation from the long-term equilibrium in the previous
period.
To examine the long run impact of shocks to the exchange rate and trading partner
income an autoregressive distributed lag model is estimated. The long run income
elasticity for total service exports is 1.2 and this is consistent with the estimates
of Marquez (2006) and Cheng (2016). The long run exchange rate elasticity for
total service exports is -1.0. This suggests that Australian service exports are
more sensitive to the exchange rate than U.S services. As hypothesised, the
aggregate elasticities hide a great deal of variation among the individual service
export categories. For example, the exchange rate elasticity estimates range from
-0.4 (education) to -2.5 (telecommunication).
The primary contribution of this thesis is that, to the best of my knowledge, it is
the first empirical study of disaggregated Australian service exports. I find that
service exports are not homogeneous products. They respond very differently to
changes in foreign income and the exchange rate. An implication of this is that,
as the world economy grows, all service industries will benefit but they will do so
unequally. The travel sector stands to increase the most and the transport sector
the least. A second implication is that government policies that depreciate the
Australian dollar will only be effective at boosting certain service exports. I find
that telecommunication services are very sensitive to currency fluctuations but that
education exports respond little.
3
Chapter 2
Literature Review
2.1 Export Demand
International trade is one of the oldest areas of study in economics and there is
an extensive body of literature on the trade in goods. Most studies assume that
exports are not perfect substitutes for domestic goods. This allows countries to
be both importers and exporters of the same good and for there to be differences
in the price of the domestic good and foreign good (Goldstein and Khan, 1985).
The demand for a particular country’s export is then a function of trading partner
income and the price of the good. Beginning with Houthakker and Magee (1969) and
continuing to the present, this specification is estimated empirically in the following
manner:
Log(exportt) = β0 + β1Log(incomet) + β2Log(pricet) + µt
Where income refers to the income of the purchasing country and price refers to the
price of the export. The coefficient on income gives the elasticity of income demand:
the percentage change in exports when the trading partners income rises by one
percent. Similarly, the coefficient on price gives the price elasticity of the export:
the percentage change in exports when the price of the export rises by one percent.
Houthakker and Magee estimate the income elasticities of imports and exports for
26 countries and find that, for certain countries, the income elasticity of imports
was different to the income elasticity of exports; a finding that helped explain the
current account of these countries. For instance, in the United States, the import
income elasticity was 1.5 and this is considerably larger than the export income
elasticity of 1.0. This implies, that if the U.S and world economies grow at the same
rate, the United States will import more than it exports causing a deterioration in
the current account. For other countries, such as Japan, the opposite is true: the
income elasticity is much higher for exports than imports, which is consistent with
Japan’s current account surplus.
The use of ordinary least squares to estimate this specification dominated trade
research until concerns about time trends in the variables were raised. As exports,
GDP and prices all drift upwards with time, the statistical significance of the results
4
may be only attributable to the trend. To account for this, (Senhadji, 1998) and
Senhadji and Montenegro (1999) use cointegration techniques to estimate price and
income elasticities. For imports, the income elasticities are found to be higher than
the estimates of Houthakker and Magee, while the export income elasticities are
found to be lower. The general pattern, that countries with large current account
deficits (the United States and Australia) have a higher income elasticity for imports
than exports, continues to hold.
Senhadji and Montenegro also examine differences in income elasticities across
geographical regions. They find that Asian countries have higher income elasticities
than both developed and developing countries. This indicates that, as the world
economy grows, Asian exports will grow at a faster rate. This is taken as further
evidence that the historical economic growth of Asian economies is attributable to
international trade. In contrast, the authors find that African countries have the
lowest export income elasticities. The authors do not speculate on why this is the
case but it is possibly due to the fact that African exports are primarily agricultural
goods. As countries grow and develop, their demand for agricultural goods is likely
to be outstripped by their demand for manufactured goods and commodities. The
finding suggests that as the world economy grows, African exports will not keep
pace with the rest of the worlds’ export growth.
Empirical analysis has also attempted to identify factors that determine trade
flows. This is routinely done using the Gravity Model, which derives its name
from Newton’s law of gravity. Tinbergen (1962) appropriated the equation for use
in economics and used it to link trade flows to the economic size of the trading
partner and the physical distance between them. The simplest expression of the
model is:
Tradei,j =GDPi ×GDPjDistancei,j
Where trade is the bilateral trade flow between country i and country j ; GDP is
the measure of the economic size of country i and country j and Distance is the
distance between the two countries. Empirically, the equation has proven successful.
Exports rise in proportion to the economic size of the trading partner, imports rise
in proportion to the size of the origin country and both are negatively affected by the
distance between countries (Head and Mayer, 2013). For instance, Brun, Carrere,
Guillaumont, and De Melo (2005) find that a one percent increase in distance reduces
trade by one percent. They also find that, for wealthy countries, the effect of distance
on trade has declined over time; a result they attribute to globalisation.
5
The Gravity Model has also been estimated using unilateral export trade flows. In
this situation the dependent variable is the export volume of one particular country
to a number of its trading partners. This allows the researcher to focus on the
determinants of a country’s exports rather than the determinants of exports in
general. For instance, Moldovan and Covaci (2015) use a unilateral Gravity Model
to examine Lithuania’s exports. They find that the distance between Lithuania and
its trading partners has a significant affect on many of Lithuania’s service exports.
Recent empirical literature has focused on the effects of other variables while
controlling for the effects of economic size and distance. For example, sharing
a common language or currency is typically significant in explaining trade (Head
and Mayer, 2013) whilst large time zone differences have a negative effect on trade
(Moldovan and Covaci, 2015). An interesting study by Anderson and Van Wincoop
(2003) uses the Gravity Model to examine the effect of the U.S-Canada border on
trade. Domestic trade between the U.S states and between the Canadian provinces
is compared to the international trade between the two countries. Anderson finds
that domestic trade is much larger than international trade, even after controlling for
economic size, population and distance. Anderson estimates that the international
border is responsible for reducing bilateral trade by 30%. This is surprising large
considering that the U.S-Canada border is informal by international standards.
2.2 Service Exports
In almost all of the previously mentioned research, the dependent variable was
a measure of total export value. This is a natural way to compare countries
and examine current account issues however, it is likely to lead to a significant
degree of aggregation bias. It is improbable that exports as diverse as iron ore,
heavy machinery and tourism would all be similarly affected by foreign income, the
exchange rate and the distance between the trading partners. In acknowledgement
of these issues, it is routine to use disaggregated trade data and examine the trade in
different goods and services separately. A multitude of authors have examined the
trade in goods (see Goldstein and Khan, 1985 for a review) and such is the depth
of this literature that there are even estimates of the sensitivity of German beer
exports to the exchange rate (Dreyer and Fedoseeva, 2016).1 However, research on
the trade in services has largely been ignored.
Unfortunately, there is no a priori reason why the results of studies that have
1They find that a 1% appreciation reduces beer exports by 0.43%.
6
examined goods should be generalisable to the trade in services. Indeed, the effects
of foreign demand, prices and the exchange rate on goods exports and service
exports should be different for at least two reasons. First, while the production and
consumption of goods is often separated in time, the production and consumption
of services occurs simultaneously. For instance, it would not be unusual to import
canned beans from Mexico that had been produced months prior, but flying to
Madagascar is a service export that is produced and consumed at the same time.
Second, unlike goods, once a service has been purchased it is not typically able to
be resold. For these reasons the aggregation of goods and services in trade studies
is likely to yield inaccurate results. This aggregation bias can be substantial. In
a 2016 country profile of Montenegro, the International Monetary Fund concludes
that a one percent increase in foreign income boosts service exports by 2.6% but
has no effect on the value of the goods Montenegro exports (IMF, 2016).
These recent elasticity estimates are often obtained using a cointegration framework.
If evidence of cointegration is found, there exists a stable long-run relationship
which exhibits short-run deviations. A small number of studies have used this
approach to examine services trade. For example, Hung, Viana, et al. (1995) finds
that U.S tourism exports, their price and world GDP are cointegrated. Hung and
Viana conclude that tourism trade is more sensitive to world income growth than
merchandise trade suggesting that in the future, the U.S tourism industry will
expand at a faster rate then the U.S manufacturing industry.
To assess the determinants of services trade, a number of studies instead use the
Gravity Model. As with the trade in goods, most studies find that the trade in
services is positively related to the economic size of the trading partners (Ceglowski,
2006; Walsh, 2006). In contrast, there is some debate in the role that distance plays
in the trade in services. Distance affects the trade in goods because of transport
costs. Shipping coal or heavy machinery is expensive and countries that have to
export to far away markets are at a disadvantage. It is not clear that the same
reasoning is applicable to services, as services are rarely transported.2 On the other
hand, physical proximity may be the primary reason services are being purchased
in the first place.
The findings in the literature are inconclusive. Ceglowski (2006) finds that a one
percent increase in the distance between trading partners reduces service trade flows
by approximately 0.8%. In contrast, Walsh (2006) and Van Nho and Huong (2014)
find that distance has no effect on service trade flows. These different conclusions
2The obvious exception being tourism.
7
are likely to be due to the use of different econometric techniques and different
data sets. Ceglowski aggregates the service trade flows of 28 countries while Van
Nho looked at the service exports of just Vietnam to the rest of the world. It
is probable that the effect of distance on services trade differs between countries.
Furthermore, although Ceglowski and Walsh use the same data set, Walsh compares
estimates using pooled OLS, random effects and the Hausman and Taylor model.
Walsh favours the Hausman and Taylor model as it corrects for correlation between
independent variables but it is only using this estimation technique that he finds
distance insignificant.
2.3 Disaggregating Services
In the limited research on the trade in services most papers have used aggregated
service data. It seems likely that the determinants of services as heterogeneous
as tourism, finance and telecommunications would be different. Indeed, Moldovan
and Covaci (2015) use a Gravity Model to examine service exports and find that
the effects of distance, time zone differences and European Union membership all
depend upon the type of service. For example, distance has a negative effect on the
export of transport services but has no effect on communication or other business
exports.
Marquez (2006) explicitly assesses the impact of using aggregate data in service trade
studies by comparing U.S service imports and U.S service exports. Using aggregate
data, the income elasticity of exports is roughly similar to the income elasticity of
imports (1.3 and 1.5). However, this masks the variation across service categories.
Private service exports have an export income elasticity of 3.2 whilst travel exports
have an income elasticity of 1.5. For both categories, the export elasticity was
larger than the import elasticity. This implies that, as the world economy expands,
service exports will outweigh service imports and that the U.S service industries will
not benefit equally. All else is held constant, the export of other business services
will increase at more than twice the rate as the export of travel services. Marquez
concludes that using disaggregated data is the most important aspect of econometric
modelling in service trade studies.
Following Marquez, Cheng (2016) compares disaggregated and aggregated U.S
service exports using a cointegration approach and three different trade models.
All models supported the conclusion that effect of income and prices on trade was
largely dependent on the type of service. Cheng finds that the use of aggregate
service data disguises the effect that changes in the exchange rate have on service
8
trade. In the aggregate, service trade is significantly affected by the exchange rate
but certain components, such as intellectual property and insurance and pension
services, are not affected by changes in the exchange rate in the short run or long
run.
2.4 Disaggregate Australian Services
Despite its importance to the Australian economy, there has been no study that
has analysed the demand for Australian service exports. Kouparitsas et al. (2017)
come close as they model the demand for Australian service exports, but they use
aggregate service data. They also assume that the foreign income elasticity is one
in order to estimate the elasticity of substitution of Australia’s service exports.
Most of the limited research on disaggregated services trade has been centred on
the exports of the United States. The results from United States data may not be
applicable to Australia, due to the different composition of our exports, the financial
dominance of the United States and the differences in our primary trading partners.
Yet, if Australia is to shift from exporting commodities to exporting services it is
important to understand the determinants of these exports and, in particular, how
they are affected by world economic growth.
This paper will add to the sparse literature on the Australian service export sector.
First, I estimate a Gravity Model to see if Australian service exports can be
explained using standard trade variables. Second, I test for cointegration between
each category of export, the exchange rate and trading partner income. Third, I
estimate an Autoregressive Distributed Lag model to obtain the long run income
and exchange rate elasticities. Finally, I estimate an Error Correction Model to
examine the short run dynamics of service exports, the exchange rate and trading
partner income.
These estimates are important because the responsiveness of service exports to
global income has implications for Australia’s balance of payments. Will rising
service exports be able to outweigh the rise in imports as the Australian economy
grows? If not, and if commodity exports also decline, Australia’s current account
will deteriorate further. Furthermore, policies that aim to increase exports by
depreciating the exchange rate assume that service exports will respond to this
depreciation. As services have only been studied in the aggregate, this assumption
has not been tested for any particular service category. It is important to know
which of these service exports are responsive in order to assess the effectiveness of
9
policy changes designed to stimulate demand for Australia’s exports.
10
Chapter 3
Approach and Variables
3.1 Approach
This paper makes use of two distinct data sets on disaggregated service exports and
this leads to a broad division in the methods used to examine them. The first data
set is panel data on disaggregated Australian service exports to particular countries.
For example, it provides the value of Australian tourism exports to China from 2000
to 2016. I use this data set to estimate a Gravity Model. The advantage of this
data set is that it provides a rich source of information on Australia’s service exports
across time and destination. The disadvantages of this data set are that the sample
size is small and that certain export categories are missing. Hence, I make use of a
second, time series data set. This provides information on Australia’s disaggregated
service exports to the rest of the world over a 45 year period. For example, the value
of Australian tourism exports to the rest of the world in 2001. I use this data set to
estimate a variety of time series models.
3.1.1 Gravity Model Variables and Data Sources
I estimate Gravity Models using five variables: Trading Partner GDP, Australia’s
GDP, Distance, Common Language and Service Exports. A description of these
variables and data sources are provided below.
Service Exports
Services are considered an export when a foreigner purchases an Australian service
even if the individual is in Australia. Data on service exports is sourced from the
Australian Bureau of Statistics (ABS).1 It is annual data, measured in Australian
dollars at current prices and the sample period is from 2000 to 2016. The gravity
model is estimated for seven different service exports. The following list provides a
short description on what each category of export includes.
(i) Finance: The purchase of Australian financial products or services.
(ii) Insurance and Pension: The purchase of insurance policies. Includes pension, life
and freight insurance.
1Catalogue 536805500405
11
(iii) Other Business: A diverse category that includes payments relating to research
and development, management consulting, legal, accounting and waste treatment
services.
(iv) Telecommunication, Computer and Information Systems: The purchase of
telecommunication, computer or information services.
(v) Transport: Includes expenditure on all forms of transportation (by sea, air and
land) for passengers and freight. Also includes postal and courier services.
(vi) Travel: Expenditure on business and personal travel. Education services are a
sub-category.
(vii) Aggregate: The total value of services exports of Australia. It includes a number
of smaller service categories that are not examined in this thesis.
Trading Partner GDP
Trading partner GDP is measured in current United States’ dollars and is the sum
of the GDP of Australia’s 23 largest service export destinations. The countries
include: Canada, China, France, Germany, Hong Kong, India, Indonesia, Ireland,
Italy, Japan, South Korea, Malaysia, Netherlands, Norway, Singapore, Philippines,
United States, United Kingdom, Vietnam, Sweden, South Africa and Switzerland.
The source is the World Bank database and the sample period is 2000 to 2016.
Australia’s GDP
Australia’s GDP is measured in current United States dollars. The source is the
World Bank database and the sample period is 2000 to 2016.
Distance
Distance is the kilometre distance between a country’s capital city and its trading
partner. The source is the French database: Centre d’Etudes Prospectives et
d’Informations Internationales (CEPII). It is calculated using the great circle
formula which measures the minimum distance between two points along the Earth’s
surface.
Common Language
Common language is a dummy variable equal to one if the trading partner speaks
English as an official language. The source is also the CEPII database.
12
3.1.2 Time Series Variables and Data Sources
I also analyse Australia’s service export demand using a time series approach over
a 45 year period. One issue with this strategy is that these variables trend upwards
with time. For example, both exports and GDP have risen considerably since 1972.
I deal with this problem by modelling service export demand in a cointegration
framework. This is consistent with other studies that have looked for cointegration
in trade. For example, Senhadji and Montenegro (1999) find the that for many
countries there is a stable relationship between total exports, export prices and
world GDP. My approach follows that of Cheng (2016) who finds that U.S service
exports, world GDP and the exchange rate have a cointegrating relationship. The
three variables I use for the time series analysis are the Trade Weighted Index,
Trading Partner GDP and Service Exports. A description of these variables and
data sources are provided below.
Service Exports
Ten different service exports categories are examined. Seven of these are identical
to the categories described above. The additional three categories are:
(i) Education: personal travel for educational reasons. Are a sub-category of travel
exports.
(ii) Government: Includes payments relating to embassies, consulates and military
agencies.
(iii) Tourism: As defined by the ABS, it is not a distinct category. It is calculated by
the ABS as the addition of personal travel services and passenger transport services.
Figure 3.1 plots a selection of the categories of services as a percentage of total
service exports. Travel services are by far the largest category while transport’s
share has fallen. The data on exports is quarterly, measured in Australian dollars
at current prices and the sample period of the data set is from 1972 to 2016. The
source is the ABS.2
Trade Weighted Index
The trade weighted index (TWI) is a measure of the nominal exchange rate. It is
calculated by the Reserve Bank of Australia and measures changes in the Australian
dollar relative to a basket of currencies weighted by the size of their trading
relationship with Australia. The source is the ABS.3
2Catalogue 5368011a3Catalogue 5302019
13
Figure 3.1: Composition of Australian Service Exports
Trading Partner GDP
Trading partner GDP is measured in current United States dollars and is the sum of
the GDP of Australia’s 23 largest service export destinations. The countries used to
calculate the measure are the same as those used in the gravity model. The source
is the World Bank database and the sample period is from 1972 to 2016.
All variables, except the language dummy, are expressed in their log value in order
to obtain percentage changes.
14
3.1.3 The Use of Nominal Data
Throughout the paper, I generally use data that measures variables in nominal
terms. This is not ideal as it is difficult to determine whether export values have risen
due to an increase in prices or an increase in volume. Working with real measures
would be preferred except that they are not available for most of Australia’s service
exports.
For the time series data, real measures of five service exports are published. The
exports are education, government, insurance and pension and travel services. I
repeat my time series analysis using this real data to check the robustness of my
nominal findings.
The nominal exports could have been deflated as is in Houthakker and Magee (1969)
or Marquez (2006), however, this is problematic because most price indices available
are calculated using a basket of goods and services. Deflating individual service
exports using these aggregate price indices would substantially bias my estimates
(Goldstein and Khan, 1985). To ensure consistency with the export data and to
explore nominal trade relationships I also use measures of nominal GDP and the
nominal exchange rate even though real measures are readily available.
15
Chapter 4
The Gravity Model
As foreshadowed earlier I begin with a preliminary analysis of the service data using
a Gravity Model. The Gravity Model takes advantage of cross-country variations
in trade flows to examine what determines a country’s trade. It has been used
successfully to explain the determinants of the trade in disaggregate goods and
trade in aggregate services. To the best of my knowledge it has never been applied
to disaggregated Australian services. The simple model in this paper fills this
research gap and confirms that disaggregated Australian service exports have the
same determinants as goods exports.
4.1 The Model
The specification I use follows Tinbergen (1962) but only considers the exports of
Australia rather than bilateral trade. The equation I estimate is as follows:
ln(XAu,j,t) = β0 + β1ln(GDPAu,t) + β2ln(GDP ∗j,t)− β3ln(DistanceAu,j) + β4Lang + εt
Where X is the service export and the index Au, j, t, indicate the value of exports
from Australia to country j at time t. GDPAu is the GDP of Australia and GDP*
is the GDP of the trading partner. GDP is the measure of the economic size of the
country and represents the quantity of exports that the country is able to supply
and demand. The variable Distance is the kilometre distance between Canberra and
the capital city of country j. Lang is a dummy variable equal to one if Australia’s
trading partner speaks English as an official language. Its addition is a common
modification of the original specification.
Sharing a common language with a trading partner is expected to have a positive
effect on the trade in services since this is a robust finding in the literature for both
the trade in goods (Head and Mayer, 2013) and in services (Moldovan and Covaci,
2015; Walsh, 2006). Indeed, Walsh found that it was one of the most important
determinants of services trade. This is because having a common language is often
viewed as a proxy for historical ties (Head and Mayer, 2013) or a measure of the
ease of doing business. In this context, it is expected to be important because the
16
buying and selling of services requires a high degree of communication.
I expect that the economic size of Australia and of Australia’s trading partner
will be positively related to Australia’s export of services. A positive relationship
between GDP and exports is consistent finding in the literature (Head and Mayer,
2013). Larger economies should require more services and should have the capacity
to supply more services. As this paper only examines the export of Australian
services, rather than bilateral trade, it is expected that the effect of trading partner
GDP will be larger than the effect of Australia’s GDP.
The effect of distance on exports is expected to be negative as distance is usually
a proxy for transaction costs such as transportation (Head and Mayer, 2013).
Although services rarely need to be transported, Kimura and Lee (2006) find that
the trade in services is more affected by distance than the trade in goods. This is
possibly due to the fact that the provision of services requires physical proximity.
However, as Kimura and Lee acknowledge, their use of aggregate service data may
hide the fact that distance has heterogeneous effects on different services.
The Gravity Model was estimated using pooled OLS and random effects methods.
These methods are commonly applied to the Gravity Model (Walsh, 2006) but have
well-known drawbacks. They require the improbable assumption that unobserved
factors do not affect the explanatory variables. In this context that would mean that
unobserved factors of a country, such as its productivity, do not affect the country’s
GDP. Alternatively, the equation could have been estimated using a fixed effects
model. This would have been statistically superior as it would have removed the
effects of the unobserved factors that affect exports. However, its implementation
would also remove the interesting, non-time varying determinants: distance and
common language. Although far from perfect, pooled OLS and random effects
provide a sufficient means of examining whether the trade in services is determined
by the same variables as the trade in goods.
The pooled OLS estimates can be seen in table 4.1. The estimates using the
random effects model are reasonably similar and are reported in the appendix
(table A.1). Heteroscedasticity robust standard errors are used throughout. As
hypothesised, trading partner GDP has a large positive effect on Australia’s export
of services. For all services except telecommunication services, the coefficient on
trading partner GDP is significant at the 5% level. For each of these categories,
the effect of increasing trading partner income is moderate; ranging from 0.34
(telecommunication) to 0.69 (insurance and pension). In contrast, Australia’s GDP
17
Table 4.1: Gravity Model
ExportEstimated Coefficients
(Robust Standard Errors)GDP∗ GDPAu Dist Comm Lang c R2
Aggregate 0.56∗ 0.05 −1.37∗ 0.67∗ 16.69∗ 0.68(0.07) (0.08) (0.19) (0.25) (2.41)
Finance 0.46∗ 0.24 −0.87∗ 0.98∗ 5.52 0.22(0.18) (0.27) (0.41) (0.63) (6.49)
Insurance & Pens 0.69∗ −0.14 −2.95∗ 0.71 26.89∗ 0.64(0.14) (0.16) (0.43) (0.38) (4.60)
Other Business 0.44∗ 0.40∗ −1.07∗ 1.26∗ 4.69 0.41(0.14) (0.16) (0.31) (0.45) (3.52)
Telecomms CIS 0.34 −0.06 −0.70∗ 1.21∗ 15.26∗ 0.28(0.19) (0.23) (0.37) (0.45) (5.87)
Transport 0.44∗ −0.34∗ −1.32∗ 0.33∗ 28.05∗ 0.34(0.05) (0.11) (0.11) (0.11) (2.93)
Travel 0.56∗ 0.02 −1.47∗ 0.41 17.81∗ 0.66(0.09) (0.10) (0.20) (0.25) (2.82)
*p-value < 0.01Sample 2000 - 2015
does not appear to have much of an effect on the export of most categories of
services as the estimates are not statistically different from zero at the 1% level. The
exceptions are other business and transport services. Strangely though, transport
exports are found to decline when Australia’s GDP rises. It is not clear why this
would be the case but it implies that as Australia gets wealthier, foreign companies
and individuals stop using Australian air and freight transport.
The relationship between distance and Australia’s aggregate service exports after
controlling for economic size can be seen in figure 4.1. As hypothesised, the distance
between Australia and its trading partner has a negative effect on Australia’s
exports. The effect is strong. If all else is held constant, a one percent increase in
distance reduces service exports by at least 0.7% (telecommunication) and at most
2.9% (insurance and pension). The coefficient on distance is statistically significant
for all categories of service exports at the 1% level.
These results support the argument that distance significantly and negatively affects
service trade despite the absence of transportation costs. My estimate for the effect
of distance on aggregate services exports is -1.3. This is similar to Moldovan and
Covaci (2015), who find that the effect of distance on aggregate Lithuanian service
exports is -1.5. These estimates are considerably larger than those obtained from
examining bilateral trade flows (import and export data). Ceglowski (2006) utilises
18
Figure 4.1: Relationship Between Aggregate Service Exports andDistance
import and export data on 28 countries to estimate that total service trade is reduced
by 0.85% when the trading countries are 1% further apart. This suggests that
Australian service imports are not greatly affected by distance; a topic for future
studies.
Sharing a common language with the trading partner has a positive effect on all
categories of service exports but the magnitude of the effect varies. Other business
services are particularly influenced by sharing a common language; rising by 1.3
percent if all else is held constant. This is not surprising considering this export
includes professional services such as consulting and legal advice, which rely heavily
on verbal communication. Unexpectedly, sharing a common language does not have
a statistically significant effect on the export of travel services. Together, these
results imply that people decide to come to Australia based more on their wealth
and proximity rather than because of a shared cultural history or because they can
easily communicate with locals.
Taken together, these results are strongly supportive of the argument that service
exports are determined by the same factors as goods exports. The results are in line
with expectations: all else held constant, countries that are wealthy, physically close
and speak English as an official language will demand more Australian services than
those that do not fit these characteristics. The most important finding is that the
19
effect of these variables on exports depends upon the service category. Thus, it would
be misleading to assume the aggregate estimates apply to all service categories.
20
Chapter 5
Cointegration
Although the Gravity Model is useful, further analysis is constrained by lack of
data. There are only 16 years of panel data that describes the destination of
disaggregated Australian service exports. Furthermore, some categories of services,
such as education, government and tourism, are missing. As a primary aim of this
thesis is to look at the long run determinants of services I continue my analysis using
the second, time series data set. This includes a greater number of service export
categories and provides the value of service exports to the rest of the world over a
45 year period. I begin by empirically testing for cointegration.
5.1 Stationarity
Cointegration analysis was introduced by Engle and Granger (1987) and it takes
advantage of the fact that some time series variables share a common trend. If a
variable has a stochastic trend, its probability distribution depends upon time and
it is said to be non-stationary (Stock and Watson, 2003). Engle and Granger show
that if a number of non-stationary time series are combined it may be possible to find
a combination of the series that is stationary. When this happens the relationship
is said to be cointegrated. A cointegrated relationship implies that there exists a
stable, long term relationship between the variables (Enders, 2010). This, is turn,
implies that past deviations from the long run equilibrium will impact future values
of the variables.
I test for a cointegrating relationship between world GDP, the exchange rate and
disaggregated Australian service exports. If one is found, past values of world GDP
and the exchange rate can be used to predict future Australian service exports.
The first requirement for cointegration is that the variables are non-stationary due
to a unit root. A plot of Australia’s service exports, figure 5.1, suggests none of them
are stationary variables. I empirically test whether service exports, world GDP and
the exchange rate are non-stationary using Augmented Dickey Fuller (ADF) and
KPSS tests.
21
Figure 5.1: Australia’s Service Exports
For the ADF test, the following equation is estimated:
∆yt = a0 + a1yt−1 +n∑i=1
βi∆yt−i + εt (5.1)
Where, y is the series that is being tested. The coefficient of interest is a1. The
null hypothesis is that a1 is equal to zero. If it is, the equation is entirely in first
differences and there is evidence that the series has a unit root (Enders, 2010). If
the null hypothesis is rejected, the residuals do not have a unit root and hence, are
stationary. Equation 5.1 requires the correct number of lags to be included. I select
the lag length using the Akaike Information Criterion (AIC).
As noted ealier, a plot of the data suggested that each series trended upward with
time. Hence, I also estimate equation 5.1 with a deterministic time trend included
as an explanatory variable. If this is the actual data generating process the power to
detect a unit root will increase with the inclusion of this trend term. I also conduct
a KPSS test on each series as proposed by Kwiatkowski, Phillips, Schmidt, and Shin
(1992). In the KPSS test, the null hypothesis is that the series is stationary. This is
22
the opposite null to that of the ADF test. The test involves the following equation:
yt = a0 + βt+ γt∑i=1
zi + εt (5.2)
Where y is the series that is being tested and zi is a random walk process. The null
hypothesis is that γ is equal to zero. If so, the series is stationary if β = 0 and trend
stationary if β 6= 0 (Greene, 2003). The ADF and KPSS test statistics can be seen
in table 5.1.
Table 5.1: ADF and KPSS Tests
Dependent Variable ADF KPSSNo Trend Trend No Trend Trend
Aggregate -3.52* -1.10 0.83* 0.22*Education -3.54* 1.21 0.85* 0.23*Finance -2.79 -3.18 0.67* 0.13*Government -4.57* -2.45 0.82* 0.22*Ins. Pens. -1.96 -1.76 0.70* 0.19*Other Business -1.45 -2.62 0.83* 0.115Tele. CIS. -1.85 -2.84 0.59* 0.11Tourism -3.64 -0.75 0.82* 0.23*Transport -5.67 -1.50 0.75* 0.23*Travel -4.08 -1.05 0.83* 0.23*GDP -2.25 -1.65 0.85* 0.21*Exchange Rate -2.73 -1.97 0.433 0.21*Critical Value 5% -2.93 -3.52 0.46 0.15
Sample 1972 - 2016*Exceeds critical Value
Critical values derived by Dickey and Fuller (1979) and Kwiatkowski et al. (1992)
The majority of the ADF tests do not reject the null hypothesis of a unit root. The
majority of the KPSS tests do reject the null hypothesis of stationarity. This is
strong evidence that all the variables are non-stationary. As expected, the inclusion
of a trend term is an important determinant of these results.
I also conduct ADF tests on the five service exports for which real data is available.
The results are in appendix table B.1. The results are consistent with the nominal
data. There is evidence at the 5% level that the variables are non-stationary and the
inclusion of a time trend is an important determinant of this conclusion. Finally, I
conduct the ADF and KPSS tests on the first difference of each series to ensure they
are not second order integrated. The results confirm the series are first difference
23
stationary and can be seen in the appendix tables B.3 and B.4.
5.2 Test for Cointegration
As the variables are non-stationary it is possible for there to be a cointegrating
relationship between the service export, the exchange rate and trading partner GDP.
I test for cointegration using the Engle-Granger two step methodology. In the first
stage, the following equation is estimated by ordinary least squares for each category
of service export:
ln(Exportt) = β0 + β1ln(Yt) + β2ln(ERt) + εt (5.3)
Equation 5.3 represents the long run relationship between the service export, GDP
(Yt) and the exchange rate (ERt). If this relationship is cointegrated, the residuals,
εt, must be stationary. Hence, in the second stage, I test for a unit root in the
residuals using an ADF test. If the null hypothesis of a unit root can be rejected,
the long run relationship is cointegrated. Again, as GDP and exports drift upwards
with time, I re-estimate the long run equation (eq. 5.3) with the inclusion of a linear
time trend. I then redo the ADF tests on the new residuals.
Table 5.2: Engle-Granger Tests
Dependent Variable Test StatisticsNo Trend Trend
Aggregate -2.72 -3.48Education -4.27* -3.48Finance -3.48 -3.47Government -5.12* -5.12*Ins. Pens. -2.78 -2.79Other Business -2.79 -2.97Tele. CIS. -2.96 -4.07Tourism -3.20 -2.11Transport -3.25 -2.92Travel -2.52 -2.24Critical Value 5% -3.92 -4.13
Sample 1972 - 2016*Exceeds critical value
Critical values derived by MacKinnon (1990)
The results of the Engle-Granger tests are in table 5.2. Without the inclusion of
a linear trend term only education and government services could reject the null
24
hypothesis of the residuals having a unit root at the 5% level of significance. With
the inclusion of a trend, only government services could reject this null hypothesis.
Using the available real data confirms these findings, only government services are
statistically significant at the 5% level. The results are in appendix table B.2. Thus,
only for government services is there evidence for a cointegrating relationship.
For illustrative purposes, in figure 5.2 I plot the estimated residuals of the long run
equation (eq. 5.3) when the export is government services and when it is other
business services. The residuals from government exports are undoubtedly more
stable then the residuals of other business exports. However, the residuals of other
business services are clearly not trending upwards or downwards.
Figure 5.2: Cointegrating Errors
Taken together, the results suggest that the majority of service exports are not
cointegrated with GDP and the exchange rate. This is irrespective of whether a
trend term is included in the long run equation. However, the residual plots do
not indicate that they are trending. Thus, it is possible that the failure to detect
cointegration may be due to the low power of the Engle-Granger test or the limited
number of observations in my data set.
25
Chapter 6
Autoregressive Distributed Lag Model
In this chapter, I examine Australia’s service export demand relationships using an
Autoregressive Distributed Lag (ARDL) formulation as developed by Pesaran and
Shin (1998). The ARDL framework is useful because it provides another, more
powerful means of testing for the presence of a cointegrating relationship between
exports, GDP and the exchange rate. It also provides estimates of the long run
income elasticity of demand and the long run exchange rate elasticity for each
category of service export.
6.1 The Model
The ARDL specification has a number of desirable features. Its dynamic structure
allows lagged values of the export as well as lagged values of GDP and the exchange
rate to effect current exports. This is an appealing property given that many service
contracts are written in advance and that it may take time for exporters to be
affected by fluctuations in the exchange rate. The model has been used widely in
the trade literature to estimate elasticities. Senhadji and Montenegro (1999) find
that African nations have the lowest export price elasticities whilst Marquez (2006)
finds that U.S services exhibit asymmetric import and export income elasticities.
The specification I use follows Cheng (2016) closely.
∆Xt = α0 + β1Xt−1 + β2Yt−1 + β3ERt−1+ (6.1)ρ∑j=0
α1∆Xt−j +
ρ∑j=0
α2∆Yt−j +
ρ∑j=0
α3∆ERt−j + εt
Where X is the service export, Y is trading partner GDP and ER is the exchange
rate.1 ∆ denotes the first difference operator, t denotes the time period and the
number of lags is ρ. I use a general to specific approach to determine the appropriate
number of lags in the model. Lag selection is made using the Akaike Information
Criterion (AIC) where a lower AIC is preferred. I test for serial correlation in the first
order residuals of each ARDL model using the Breusch-Godfrey Lagrange Multiplier
(LM) test. All variables are in log values.
1Measured by TWI
26
In the long run, the change in exports, GDP and the exchange rate will be zero.
Therefore, the long run elasticity with respect to world income and the exchange
rate can be derived respectively using:
ηIncome =β2β1
ηExchangeRate =β3β1
The long run income elasticity is expected to be positive: a rise in trading partner
GDP should increase Australia’s exports. The long run exchange rate elasticity is
expected to be negative: an appreciation in the exchange rate should reduce service
exports.
I again test for cointegration between the export, trading partner GDP and the
exchange rate using a bounds testing procedure as proposed by Pesaran et al.
(2001). This method uses an F-test to test for the joint significance of the one
period lag of the explanatory variables in equation 6.1. The null hypothesis, H0,
is: β1 = β2 = β3 = 0. If the null hypothesis can be rejected, there is evidence for
a cointegrating relationship. The critical values of the bounds test are supplied by
Pesaran et al. (2001). The lower bound assumes all variables are I(0) and the upper
bound assumes they are all variables are I(1). If the test statistic is smaller than
the lower bound, the null is not rejected. If it is larger than the upper bound, the
null is rejected. If it falls within the bounds, the test is inconclusive.
6.1.1 Nominal Data
Table 6.1 provides the long run income and exchange rate elasticities and the bounds
test for cointegration. It also provides the lag structure of each ARDL equation.
For example, aggregate services has three lags of the dependent variable, two lags
of trading partner GDP and four lags of the exchange rate. In the final coloumn is
the p-value of the Breusch-Godfrey LM test for serial correlation. I find no evidence
of serial correlation in any of the ARDL equations at the 1% level.
For all categories of services, a rise in trading partner income is associated with a rise
in Australian exports. For all categories of services except other business services,
an appreciation in the exchange rate reduces Australian exports. As hypothesised,
the aggregation of services masks a great deal of variation between the service
exports. The long run income elasticity for total service exports is 1.2% and the
long run exchange rate elasticity is negative 1%. Yet, a one percent increase in
27
Table 6.1: Long Run Elasticities Nominal ARDL Model
ExportLong Run Elasticity Bounds Lag LMGDP ER Test Structure p-value
Aggregate 1.18 −1.00 6.68∗∗ 3, 2, 4 0.59Education 2.13 −0.32 12.08∗∗ 3, 0, 0 0.91Finance 1.71 −1.76 3.71∗ 3, 2, 0 0.48Government 0.66 −0.33 8.24∗∗ 3, 1, 0 0.57Insurance & Pens 0.86 −2.43 6.21∗∗ 4, 3, 3 0.06Other Business 1.49 0.35 4.43∗ 2, 0, 0 0.38Tele CIS 1.33 −2.45 6.02∗∗ 4, 2, 2 0.43Tourism 1.39 −1.37 19.57∗∗ 5, 1, 2 0.50Transport 0.44 −1.55 14.02∗∗ 1, 0, 4 0.73Travel 1.55 −1.21 20.73∗∗ 4, 0, 0 0.08
**Exceeds 5% critical values [3.79, 4.85]*Exceeds 10% critical values [2.86, 3.53]
Critical values derived by Pesaran et al. (2001)
foreign income is associated with only a 0.4% rise in transport services but a 2.1%
increase education exports. Similarly, the effect of a one percent appreciation of
the exchange rate ranges from -0.32% (education) to -2.5% (telecommunication and
information systems).
I am unsure why other business exports would rise when the Australian dollar
appreciates. All other estimated elasticities appear reasonable. For instance, I find
that rising foreign income has a large positive effect on both tourism and education
exports but exchange rate changes have very different effects on each category. This
is intuitive; foreign tourists have the option of travelling to a wide variety of countries
and will do so for only a relatively short period of time. The prevailing exchange
rate will have a big influence on whether they come to Australia. In contrast,
the majority of education exports are to people wanting a university education in
Australia. This is a commitment of at least three years and one that is not often
taken later in life. For these reasons, fluctuations in the exchange rate would be
expected to have little impact on education exports.
Table 6.1 also provides the test statistic for the bounds test for each category of
service export. The null hypothesis is that there is no cointegrating relationship. For
eight of the ten categories of service exports this null hypothesis could be rejected
at the 5% level. This is evidence that the export, GDP and the exchange rate
are cointegrated. For financial service exports and other business services the null
hypothesis could be rejected at the 10% level. Thus, in contrast to the Engle-Granger
28
tests, the bounds test provides strong evidence that many of the Australia’s service
export categories are cointegrated. This is possibly due to the different small sample
properties of the tests or because the ARDL bounds test allows for the variables to
be a mixture of stationary and first difference stationary (Pesaran et al., 2001).
6.1.2 Real Data
As the long-run relationship between exports, GDP and the exchange rate is better
conceptualised in terms of real, not nominal values, I re-estimate the ARDL model
(6.1) using real data. There are only five categories of services for which this is
possible: education, finance, government, insurance and pension and travel services.
I also use real measures of the exchange rate and trading partner GDP. My analysis
is robust if the real and nominal estimates are consistent.
Table 6.2: Long Run Elasticities Real ARDL Model
ExportLong Run Elasticity Bounds Lag LMGDP ER Test Structure p-value
Education 3.71 −1.71 9.48∗ 3, 1, 1 0.62Finance 1.24 0.28 4.08∗∗ 1 ,2, 1 0.87Government 0.47 −0.24 13.36∗ 2, 0, 0 0.29Insurance & Pens −5.82 6.72 9.02∗ 4, 0, 0 0.06Travel 2.43 −1.12 7.78∗ 2, 0, 0 0.11
**Exceeds 5% critical values [3.88, 4.61]*Exceeds 10% critical values [3.38 4.02]
Critical values derived by Pesaran et al. (2001)
The long run elasticity estimates and the results of the bounds test can be seen in
table 6.2. For government exports, the real and nominal elasticities are consistent.
For the other four exports, the results using the real data are different to the results
using the nominal data. For education and travel exports, the magnitude of the
income elasticities increases while the exchange rate elasticity is similar. For financial
exports the income elasticity is smaller and the exchange rate elasticity is positive.
This implies that financial exports increase when the Australian dollar appreciates
despite the fact they are now more expensive. This does not seem plausible. More
problematically, the income elasticity for insurance and pension services is negative.
This suggests that insurance and pension exports decrease when our trading partners
get wealthier. This is also not likely. To better understand these results I plot the
five services categories in figure 6.1.
29
Figure 6.1: Service Exports (Real Data)
The first aspect to note is that the sample size is much smaller. The first real
observation is in 1986, 14 years after the first nominal observation. It is possible
that my sample size is now too small for accurate estimation. The second aspect to
note is that, while real travel, finance and education exports have risen considerably,
real government services have been almost constant over the period. This explains
the very low income and exchange rate elasticities of government exports. Finally,
real insurance and pension exports rose from 1986 to 1999 but fell from 1999 to
2016. This is surprising as trading partner income grew immensely over the entire
period. This suggests that there are unobserved factors that have affected insurance
and pension exports. Perhaps a policy change or competition from other countries
is responsible.
6.2 Inference using Dynamic Ordinary Least Squares
As exports, GDP and the exchange rate are cointegrated, the estimates of the
coefficients are consistent, however, the distribution of these estimates is not normal.
This means that inference based on the t-statistics in the long run equation (5.3)
and the ARDL model may be inaccurate (Stock and Watson, 2003).
One solution to this problem is to use dynamic ordinary least squares (DOLS)
(Stock and Watson, 1993). This procedure uses past values, present values and
30
future values of the change in variables in order to make valid statistical inference
about the estimated coefficients. The DOLS equation I estimate is:
Xt = β0 + β1Xt + β2Yt + β3ERt+ (6.2)ρ∑
j=−ρ
δj∆Xt−j +
ρ∑j=−ρ
δj∆Yt−j +
ρ∑j=−ρ
δj∆ERt−j + εt
Where, again, X is the service export, Y is trading partner GDP and ER is the
exchange rate. ∆ denotes the first difference operator, t denotes the time period
and the number of leads and lags is ρ. I use the AIC to determine the appropriate
number of leads and lags and HAC standard errors to correct for heteroscedasticity
and autocorrelation (Newey and West, 1986).
Appendix table C.1 provides the estimates of β1, β2 and β3 as well as the associated
p-values. The DOLS estimation confirms the results of the ARDL model. The
effects of trading partner GDP and the exchange rate are statistically significant
at the 5% level for nearly all categories of service exports. The only exception is
the coefficient on the exchange rate for education exports. This is not significant at
even the 10% level. This result is congruent with the low estimated exchange rate
elasticity in ARDL model. Together, the results suggest that education exports are
not affected by the exchange rate.
I note that, for most categories of services, the suggested number of leads and lags
is at least four. This is a considerable number as I have three variables and a small
sample size. A large number of leads and lags quickly reduces degrees of freedom
and this weakens my ability to draw inference.
31
Chapter 7
Error Correction Model
For most categories of services, the bounds tests provides strong evidence of
cointegration. This implies that there exists a stable, long term relationship between
trading partner GDP, the exchange rate and the service export. This in turn implies
that, if there is a deviation from the long-term equilibrium among the variables, one
or more of the variables will adjust to restore the equilibrium. In this chapter, I
use an error correction framework to estimate how quickly this equilibrium will be
restored. The Error Correction Models (ECM) also provide estimates of the short
run effects of trading partner GDP and the exchange rate on service export growth.
7.1 The Model
I develop error correction models of service export growth using the form proposed
by Engle and Granger (1987):
∆Xt = α0 + αX(Xt−1 − β1Yt−1 − β2ERt−1)+ (7.1)ρ∑j=1
α1j∆Xt−j +
ρ∑j=1
α2j∆Yt−j +
ρ∑j=1
α3j∆ERt−j + εt
Where X is the service export, Y is the trading partner GDP and ER is the exchange
rate. Equation 7.1 indicates that the growth in service exports is a function of past
periods’ GDP growth, export growth, exchange rate change and the deviation from
long run equilibrium in the previous period, (Xt−1 − β1Yt−1 − β2ERt−1).
Following from Engle and Granger (1987), the lagged residuals from the long run
equation (5.3), et−1, are used as an estimate for the expression (Xt−1 − β1Yt−1 −β2ERt−1). Hence, the error correction model I estimate for each service category is:
∆Xt = α0 + αX et−1+ (7.2)ρ∑j=1
α1j∆Xt−j +
ρ∑j=1
α2j∆Yt−j +
ρ∑j=1
α3j∆ERt−j + εt
Where, αX is the speed of adjustment coefficient that describes how quickly the
32
long run relationship is restored. If the variables are cointegrated, I expect αX to
be negative and statistically significant.
The coefficients on the lags, αj indicate the short run effects of that variable on
export growth. The appropriate lag length is determined on the basis of each
lag’s statistical significance while also ensuring that the errors, εt, are serially
uncorrelated. I test for serial correlation in the first order residuals of each ECM
model using the Breusch-Godfrey LM test. All variables are in log values.
It is important to note that, in a cointegrating relationship, it is usually assumed
that all the variables play a role in adjusting to the long run equilibrium. Hence,
in theory, the error correction model could be re-estimated with exports as an
explanatory variable and GDP or the exchange rate as the dependent variable. In
this context however, it is likely that only service exports act to restore the long
term relationship. This is because the income of Australia’s trading partners and the
exchange rate are likely to be weakly exogenous. I argue foreign income is weakly
exogenous because it is improbable that Australia’s service exports cause the GDP
growth of our trading partners. The exchange rate is also weakly exogenous in this
context because I am examining disaggregated service exports. Individually these
make up a very small proportion of total exports and as such, changes in the volume
of these service exports should not cause changes in the exchange rate.
7.2 Nominal Data
Table 7.1: Nominal Error Correction Model
∆ExporttCoefficients
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1
Aggregate −0.055 0.331∗∗∗ 0.545 −0.109Education −0.115 0.978∗∗∗ 0.480 −0.112Finance −0.346∗ 0.248 1.592 0.125Government −0.531∗∗∗ 0.162∗∗ −0.064 0.055Insurance & Pens−0.099∗∗ 0.735∗∗∗ 0.185 0.433∗∗
Other Business −0.249∗∗ 0.222 0.542 −0.162Telecomms CIS −0.209 0.480 −1.146 0.788Tourism −0.106 0.268∗∗ 0.713∗∗ −0.316∗∗
Transport −0.196∗ 0.461∗∗∗ 0.312 0.055Travel −0.011 0.056 0.592∗∗∗ −0.482∗∗∗
***p-value < 0.01, **p-value < 0.05, *p-value < 0.10
33
Table 7.1 provides the results from the ECM for all service exports categories. The
full results, with standard errors reported, can be seen in the appendix (table D.1).
For all categories, αX , the speed of adjustment coefficient is negative and reasonably
large. This indicates that the growth of service exports in the current period will be
lower if there was a positive discrepancy in the long run relationship in the previous
period. If the discrepancy is negative, the growth in services in the current period
will be higher than if there was no discrepancy. For example, if there is a deviation
from the long run equilibrium for financial exports, then 0.35% of this deviation
is corrected over the year. It is important to note that the speed of adjustment
terms vary greatly in their magnitude. While the estimate for service exports in the
aggregate is only -0.05, the estimates range from -0.01 (travel) to -0.5 (government).
Once again, this confirms the importance of using disaggregated export data.
The speed of adjustment parameters are significant at the 5% level for government,
insurance and pension and other business services. The parameters are significant at
the 10% level for finance and transport services. The lack of statistical significance
is evidence that those exports are not cointegrated with GDP and the exchange rate.
However, this may be a result of the short data set and the use of lags that further
reduce the power to detect a statistically significant result.
To assess how well the error correction models fit the data, I plot the fitted, actual
and residual values. Figure 7.1 depicts the values for other business exports, a
category for which the speed of adjustment term was significant, and the values
for aggregate services, a category for which the speed of adjustment term was
insignificant.
Figure 7.1: Fitted, Actual and Residual Plots
34
A comparison of the actual values to the fitted values in the respective models
indicates that the ECM fits the data for both categories of exports well. However,
the residuals for aggregate services are more volatile than the residuals for other
business services. This provides visual confirmation that a cointegration framework
is more appropriate for some export categories than others. It also highlights yet
again the importance of using diaggregated data. While aggregate services are not
cointegrated, some of the categories of services that make up this aggregate are
cointegrated. This information can be utilised for forecasting purposes. The plots
for the remaining service export categories can be seen in the appendix, figure D.1.
Cointegration may be better modelled with a linear time trend in the long run
equation. The inclusion of this term moderately increases the magnitude of the speed
of adjustment coefficient in the ECM for all service exports. These results can be
seen in the appendix table D.2. The speed of adjustment parameters for financial and
telecommunication services are now also significant at the 5% level. Problematically,
for transport and travel exports, the inclusion of the trend term turns the speed of
adjustment parameter positive. This is an issue because it implies that if travel or
transport exports are growing above the long run level in the previous period they
will increase in the current period. This is not congruent with cointegration and
indicates that there is no long term stable relationship.
7.3 Real Data
Table 7.2: Real Error Correction Model
∆ExporttCoefficients
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1
Education −0.148 0.561∗∗ 1.680 −0.366Finance −0.356∗ 0.044 9.879∗∗ −0.079Government−1.544∗∗ 0.493∗∗ 0.680 −0.070Ins Pens −0.244∗∗ 0.380∗∗∗ −0.266 1.236Travel −0.356∗∗ 0.188 0.263 −0.244
***p-value < 0.01, **p-value < 0.05, *p-value < 0.10
As a robustness check, I re-estimate the error correction models using real data for
the services for which it is available. The results are in table 7.2. Encouragingly, the
speed of adjustment term remains negative and three of the five speed of adjustment
coefficients are significant at at least the 5% level and one is significant at the 10%
level. A full table of results with standard errors can be found in the appendix (table
35
D.3).
Unfortunately, compared to nominal data, the use of real data changes the
magnitude of the speed of adjustment parameter. For education and financial
services this change is not substantial. For travel services, the estimate with real
data is 35 times larger than the nominal estimate and is statistically significant. I
am unsure what would cause this but it is clear that there are issues in using nominal
data for travel exports.
Using real data, the coefficient on government services is above one and significant
which does not make economic sense. It implies that if there is a positive
discrepancy in the long run relationship then there will be a correction of 150%.
This implies, that in the current period, there is a negative discrepancy in the long
run relationship. This discrepancy will then over correct by 150% in the next period
and equilibrium will never be restored. In an attempt to rectify this problem, I
estimate the government long run equation with a date dummy variable to account
for the large increase in government exports for the 2000 Olympic Games. The
speed of adjustment estimate remains greater than one although it did decrease to
1.2. The results are in appendix table D.4.
Finally, if a linear trend term is also included in the long run equation (eq. 5.3), the
estimate for the speed of adjustment term for all categories of exports changes only
slightly. The results are in appendix table D.5.
7.4 Asymmetric Exchange Rates
One potential issue with the error correction model is that it is assumed that changes
in the growth rate of the explanatory variables have symmetric effects on service
export growth. For example, say that the error correction model estimates that
when exchange rates change by 1%, export growth changes by 2%. It is implicitly
assumed that this 2% change is applicable to appreciations and depreciations; when
exchange rates depreciate, export growth rises by 2% and when they appreciate,
export growth falls by 2%.
It is possible that this is not an appropriate assumption. A small number of studies
find that exports respond strongly to appreciations but not to depreciations (Dreyer
and Fedoseeva, 2016; Elbejaoui et al., 2013). This is known as asymmetric exchange
rate effects and it may occur because when the exchange rate appreciates, it makes
export immediately uncompetitive but when they depreciate it does not lead to
36
the rapid growth and development of the export industry. If this is the case, my
estimates of exchange rate elasticities and the speed of adjustment term will be
incorrect. In the case of appreciations, the estimates will be negatively biased and
in the case of depreciations, they will be positively biased. I examine this issue by
allowing for asymmetric exchange rate effects to affect export growth. I use the
following error correction model:
∆Xt = α0 + αD(D∗∆ER)t−1 + αX et−1+ (7.3)ρ∑j=1
α1j∆Xt−j +
ρ∑j=1
α2j∆Yt−j +
ρ∑j=1
α3j∆ERt−j + εt
Where, D is a dummy variable equal to one if the exchange rate appreciated from
last year, ∆ER is the change in the exchange rate and all other variables are identical
to before. The interaction term captures the short run effects of an appreciation
of the exchange rate on service export growth. The coefficient of interest is αD. If
αD is statistically significant then appreciations of the exchange rate affect service
export growth more than depreciations do.
Table 7.3: ECM with Asymmetric Exchange Rate Effects
∆ExporttCoefficients
αD Speed of Adjust. ∆Exportt−1 ∆GDPt−1 ∆TWIt−1
Aggregate 0.013 −0.055 0.331∗∗∗ 0.545 −0.114Education −0.073 −0.113 0.978∗∗∗ 0.486∗∗ −0.083Finance −4.594∗ −0.428 0.286 1.734 2.178Government 0.210 −0.532∗∗ 0.165 −0.081 −0.031Insurance & Pens 0.112 −0.097∗∗ 0.736∗∗∗ 0.180 0.387Other Business 2.538∗∗ −0.284∗∗ 0.303∗∗ 0.335 −1.208∗
Telecomms, CIS −2.951 −0.193∗ 0.330 −1.625∗∗ 2.470∗∗
Tourism 0.221 −0.108 0.286 0.680∗∗∗ −0.396Transport 0.040 −0.195 0.463∗∗∗ 0.308 0.039Travel −0.167 −0.075 0.231 0.619∗∗∗ −0.317
***p-value < 0.01, **p-value < 0.05, *p-value < 0.10
The results from estimating equation 7.3 using nominal data can be seen in table 7.3
(the full nominal results are in appendix table D.6). At the 5% level, the only service
export category for which αD is statistically significant is other business services.
Therefore, for all other exports, I fail to find evidence of asymmetric exchange rate
effects on service export growth.
37
I repeat my analysis using the available real data and the results are in the appendix
(table D.7). The real results support the nominal results, at the 5% level, αD was
not statistically significant for any export category. This suggests that exchange
rates affect export growth in a symmetric manner and that my estimated exchange
rate elasticities are valid.
7.5 Summary
The findings of this chapter indicate that disaggregated service exports can be
modelled in an error correction framework. The speed of adjustment coefficient is
consistently negative and reasonably sized. This suggests that service export growth
will adjust downwards in the current period if it exceeded the long run growth rate
in the previous period. This information can be used to more accurately forecast
Australia’s service exports.
The size of the speed of adjustment coefficient differs greatly between the export
categories. Again, this indicates that it would be misleading to assume the estimate
obtained from aggregate data apply to all service export categories. Unfortunately,
this coefficient is not statistically significant for the majority of service categories.
This suggests that most service exports are not cointegrated with the exchange
rate and trading partner GDP. This is supportive of the Engle-Granger tests but
contradicts the results of the bounds tests.
I fail to find evidence of short run, asymmetric exchange rate effects on service
export growth and this supports the validity of my elasticity estimates. However,
the failure to find evidence of asymmetric effects could be due my analysis only
considering short run dynamics. Perhaps asymmetries affect export growth in the
long run.
38
Chapter 8
Conclusion
My results indicate that using disaggregated trade data is crucial if one wants to
understand Australia’s export of services. Utilising three data sets and a variety of
econometric approaches, I find that different service exports respond very differently
to the exchange rate, foreign income, distance between Australia and its trading
partners and whether those partners speak English as an official language.
The lack of research into disaggregated services means there with a few studies
with which to compare my results for Australian services to their international
counterparts. Only Marquez (2006) and Cheng (2016), who examine American
service exports are comparable. At an aggregate level, my estimate of the long run
income elasticities for aggregate service exports is 1.2. This is reasonably consistent
with Marquez’s estimate of 1.0 and Cheng’s estimate of 1.3.
At a disaggregated level, Australian and U.S long run income elasticities are similar.
I find that a one percent increase in trading partner income increases Australian
transport exports by 0.4% while Cheng (2016) finds American transport exports
rise by 0.6%. Australian and American telecommunication, insurance and financial
services are similarly affected by rising foreign income. A notable difference between
the two countries’ exports is travel services. I estimate Australian travel services rise
by 1.6% in response to a one percent increase in foreign income. However, Cheng
estimates that U.S travel exports rise by only 0.7% in response to the same change
in foreign income.
On the other hand, Australian and U.S service exports have very different long
run exchange rate elasticities. With the exception of other business services, all
Australian service exports respond more strongly to an appreciation of the exchange
rate than their American counterparts. For instance, I estimate Australian financial
service exports fall by 1.8% when the exchange rate appreciates by 1% while
Cheng estimates that American financial exports rise by 0.1% when the U.S dollar
appreciates by 1%. It is possible that the U.S dollar’s role as the world’s reserve
currency makes U.S exports less exposed to currency fluctuations compared to the
Australian service exports. These differences between my estimates for Australia
39
and Cheng’s estimates for the U.S suggests it would be inappropriate to generalise
the findings to other countries.
My results also indicate that Australian service exports will grow significantly as the
world economy continues to grow. The income elasticity of aggregate service exports
was greater than one; suggesting that in the future, Australia’s export growth
will outpace the income growth of our trading partners. All else held constant,
the industries that should benefit most from this rise are travel exports (including
education) and financial exports. Transport industries are expected to benefit the
least.
Australia’s goods exports are also expected to grow faster than world income.
Senhadji and Montenegro (1999) estimate that the long run income elasticity for
Australian exports is 2.6, while Norman (2007) estimates that the elasticity of
Australian manufactured goods is 2.2. These estimates are larger than my estimated
income elasticity of aggregate services. This suggests that Australian service exports
are less responsive to changes in world income than goods exports. This may reflect
the fact that Australia’s major trading partners are developing economies (primarily
in Asia) and Australia’s major manufacturing exports (iron, steel and machinery) are
inputs into production. Perhaps as Australia’s trading partners grow, they require
far more of these types of goods then they require services.
I find mixed evidence that individual service exports are cointegrated with world
GDP and the exchange rate. The statistical success is dependent upon the type of
service, the econometric method and whether nominal or real data is used. However,
no evidence was found that aggregate services were cointegrated. This suggests that,
when forecasting service exports, disaggregated data should be used in order to
improve accuracy. If the service categories can be assumed to be cointegrated, my
estimates suggest that the exports adjust quickly to deviations from their long run
growth rate. For most categories of services, approximately 10 to 20 percent of this
disequilibrium will be corrected in the first year.
I also find that distance negatively affects Australian service exports. Aggregate
service exports are reduced by 1.37% when the distance of the trading partner
increases by 1%. This is comparable to the effect of distance on goods; Brun
(2017) estimates that distance reduces goods trade by 1.35%. This finding supports
the argument that the physical proximity of the trading partner is an important
determinant of service trade. However, it remains to be seen through what
mechanism distance is operating. For travel and transport services, the mechanism
40
would be the same for that of goods: distance would reflect transport costs. Yet
most other services do not incur significant transportation costs. Is distance actually
a proxy for time zone differences or does it reflect the fact that countries close to
Australia are more knowledgeable of Australia’s business environment? These are
questions for future studies.
In comparing nominal data to real data, I find that my results change; sometimes
dramatically. For example, using real but not nominal data, there is evidence that
travel exports are cointegrated. And using real data the estimated income elasticity
is almost 60% larger than the estimated income elasticity using nominal data. This
suggests that my nominal findings are conflated by movements in export prices and
export value. However, I was unable to investigate this further as real measures
were not available for the majority of service categories.
Real measures are not available because price indices of disaggregated services are
not published. This leads to a second concern: in the literature, it is common to
specify export demand equations as a function of prices as well as foreign income.
This was not possible for my analysis and, as such, I have omitted any consideration
of how the price of Australian service exports influences their demand. In order to
conduct future research and to check the robustness of my results, improvements
will need to be made in the data coverage of service exports.
Some of my findings raise more questions than they answer. Although it is reasonable
to find that industries vary in their response to foreign demand, why has the
enormous growth in global income over the past 40 years benefitted some Australian
services so much more than others? For instance, travel exports have grown rapidly
while insurance and pension services have declined from their peak 16 years ago.
Is the inexorable rise in travel exports solely attributable to the growing wealth in
Asian economies? Or, are there domestic and international policy changes that are
responsible? The fact that insurance and pension exports have declined suggests
there are other factors at play. These are critical considerations for future research.
The results of this paper provide the first insight into the Australian service export
industry. They are an initial step in understanding how Australia can shift from
exporting commodities to exporting services. I find that there are vast differences
in how Australian services respond to standard trade variables. However, as all
Australian services have moderately large exchange rate elasticities, government
policies that depreciate the Australian dollar should have a positive impact on
the service export industry. Furthermore, my results indicate that the use of
41
disaggregate service data should be an important consideration in future research
and could improve the accuracy of service export forecasts.
42
Appendix A
Gravity Model
Table A.1: Gravity Model Using Random Effects Estimation
ExportEstimated Coefficients
(Robust Standard Errors)GDP∗ GDPAu Dist Comm Lang c R2
Aggregate 0.73∗ −0.07∗ −1.60∗ 0.65∗∗ 17.37∗ 0.67(0.16) (0.09) (0.29) (0.27) (2.28)
Finance 0.53 0.19 −0.96 0.99 5.55 0.22(0.32) (0.30) (0.59) (0.63) (6.69)
Insurance & Pens 0.75∗ −0.17∗ −3.04∗ 0.71∗∗ 27.27∗ 0.64(0.21) (0.14) (0.56) (0.39) (4.50)
Other Business 0.31∗∗ 0.49∗ −0.89∗ 1.28∗ 3.97 0.40(0.15) (0.17) (0.34) (0.48) (3.62)
Telecomms CIS 0.50∗ −0.17 −0.92∗∗ 1.24∗ 15.96∗ 0.27(0.18) (0.22) (0.42) (0.45) (6.18)
Transport 0.62∗ −0.47∗ −1.54∗ 0.34 28.90∗ 0.31(0.16) (0.15) (0.38) (0.45) (4.17)
Travel 0.75∗ −0.11 −1.71∗ 0.39 18.55∗ 0.65(0.17) (0.12) (0.30) (0.27) (2.61)
*p-value <0.01, **p-value < 0.05
Sample 2000 - 2015
43
Appendix B
Cointegration
Table B.1: ADF Tests (Real Data)
Dependent Variable Test StatisticNo Trend Trend
Education -2.86 -0.21Finance -2.56 -4.53∗
Government -2.64 -4.63∗
Insurance & Pens -3.58∗ -3.45∗
Travel -2.98∗ -3.08GDP -1.51 -2.50ER -1.43 -2.28
Critical Value 5% 2.96 3.57Sample 1986 - 2016
*Exceeds critical valueCritical values derived by Dickey and Fuller (1979)
Table B.2: Engle-Granger Tests (Real Data)
Dependent Variable Test StatisticNo Trend Trend
Education -1.91 -3.10Finance -2.94 -2.69
Government -6.22∗ -5.64∗
Insurance & Pens -3.11 -3.08Travel -1.71 -3.74
Critical Value 5% -3.92 -4.13Sample 1986 - 2016
*Exceeds critical valueCritical values derived by MacKinnon (1990)
44
Table B.3: ADF Tests on the First Difference of the Series (NominalData)
Dependent Variable ADFNo Trend Trend
∆ Aggregate -1.32 -3.89*∆ Education 0.02 -4.09*∆ Finance -4.75* -4.81*∆ Government -5.90* -7.61*∆ Ins. Pens. -2.66 -3.55*∆ Other Business -5.82* -5.88*∆ Tele. CIS. -3.47* -3.46∆ Tourism -1.23 -4.53*∆ Transport -3.70* -5.79*∆ Travel -1.38 -5.09*∆ ER -5.03* -5.32*∆ GDP -4.19* -2.70Critical Value 5% -2.93 -3.52
Sample 1972 - 2016*Exceeds critical value
Critical values derived by Dickey and Fuller (1979)
Table B.4: ADF Tests on the First Difference of the Series (Real Data)
Dependent Variable ADFNo Trend Trend
∆ Education -4.84* -4.54*∆ Finance -3.72* -3.70*∆ Government -4.39* -4.34*∆ Ins. Pens. -5.72* -5.61*∆ Travel -3.02* -4.38*∆ ER -4.18* -4.38*∆ GDP -5.73* -5.66*Critical Value 5% -2.99 -3.60
Sample 1986 - 2016*Exceeds critical value
Critical values derived by Dickey and Fuller (1979)
45
Appendix C
ARDL
Table C.1: Dynamic Ordinary Least Squares Results
ExportCoefficient Estimates S.E. of Leads,
GDP ER Constant Reg Lags
Aggregate Services 1.30 -0.60 -9.50 0.05 4, 4S.E. 0.04 0.14 1.06
P-Value 0.00 0.00 0.00
Education 2.29 0.26 -31.90 0.10 4, 3S.E. 0.07 0.17 1.55
P-Value 0.00 0.15 0.00
Finance 2.28 -4.58 -13.84 0.23 4, 3S.E. 0.72 1.67 6.44
P-Value 0.01 0.02 0.06
Government 0.84 -0.68 -5.26 0.08 4, 0S.E. 0.02 0.07 0.50
P-Value 0.00 0.00 0.00
Insurance and Pens -0.03 -3.06 20.23 0.14 4, 4S.E. 0.09 0.31 2.24
P-Value 0.74 0.00 0.00
Other Business 1.95 1.16 -30.48 0.22 4, 4S.E. 0.15 0.44 3.87
P-Value 0.00 0.02 0.00
Telecomms & CIS 1.46 -2.59 -7.18 0.08 3, 3S.E. 0.37 0.91 2.80
P-Value 0.01 0.04 0.05
Tourism 1.53 -1.03 -12.22 0.05 4, 4S.E. 0.04 0.15 1.01
P-Value 0.00 0.00 0.00
Transport 0.53 -1.34 5.16 0.06 4, 4S.E. 0.05 0.14 1.33
P-Value 0.00 0.00 0.00
Travel 1.69 -0.82 -15.99 0.05 4, 4S.E. 0.04 0.14 1.03
P-Value 0.00 0.00 0.00
AIC used to determine lead and lag lengthS.E are Newey-West (1986) HAC adjusted
46
Appendix D
ECM
Table D.1: Complete Error Correction Model (Nominal Data)
∆ExporttCoefficients LM
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-valueAggregate −0.055 0.331∗∗∗ 0.545 −0.109 0.25
(−0.072) (0.126) (0.157) (0.104)Education −0.115 0.978∗∗∗ 0.480 −0.112 0.18
(0.072) (0.112) (0.327) (0.215)Finance −0.346∗ 0.248 1.592 0.125 0.44
(0.179) (0.198) (1.021) (0.687)Government −0.531∗∗∗ 0.162∗∗ −0.064 0.055 0.85
(0.194) (0.177) (0.340) (0.213)Insurance & Pens −0.099∗∗ 0.735∗∗∗ 0.185 0.433∗∗ 0.23
(0.044) (0.109) (0.341) (0.221)Other Business −0.249∗∗ 0.222 0.542 −0.162 0.22
(−0.249) (0.222) (0.542) (−0.162)Telecomms & CIS−0.209 0.480 −1.146 0.788 0.06
(0.134) (0.242) (0.800) (0.459)Tourism −0.106 0.268∗∗ 0.713∗∗ −0.316∗∗∗ 0.36
(−0.018) (0.136) (0.488) (−0.448)Transport −0.196∗ 0.461∗∗∗ 0.312 0.055 0.75
(−0.196) (0.461) (0.312) (0.055)Travel −0.011 0.056 0.592∗∗∗ −0.482∗∗∗ 0.48
(−0.011) (0.056) (0.592) (−0.482)***p-value < 0.01,**p-value < 0.05,**p-value < 0.10
(Robust Standard Errors)Sample 1972 - 2016 (Reduced Sample for Telecomms & CIS and Finance)
47
Table D.2: Complete Error Correction Model with Trend (NominalData)
∆ExporttCoefficients LM
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-valueAggregate Services−0.107 0.358∗∗∗ 0.494∗∗ −0.067 0.16
(0.091) (0.126) (0.165) (0.112)Education −0.156∗ 0.958∗∗∗ 0.468 −0.067 0.31
(0.080) (0.112) (0.318) (0.214)Finance −0.414∗∗ 0.283 1.375 0.041 0.44
(0.196) (0.200) (1.037) (0.670)Government −0.531∗∗∗ 0.161 −0.065 0.053 0.83
(0.193) (0.177) (0.340) (0.213)Insurance & Pens −0.180∗∗∗ 0.502∗∗∗ −0.147 0.646∗∗∗ 0.15
(0.041) (0.144) (0.290) (0.188)Other Business −0.309∗∗∗ 0.262 0.570 −0.080∗∗ 0.39
(0.109) (0.148) (0.451) (0.326)Telecomms & CIS −0.687∗∗ 0.535∗∗ 0.063 0.475 0.87
(0.278) (0.239) (0.927) (0.512)Tourism −0.143 0.284∗∗ 0.668∗∗∗ −0.269 0.51
(0.099) (0.126) (0.234) (0.161)Transport 0.120 0.009∗∗∗ 0.186 0.957 0.74
(0.104) (0.154) (0.252) (0.158)Travel 0.001 0.049 0.605∗∗∗ −0.491∗∗∗ 0.47
(0.102) (0.163) (0.217) (0.156)***p-value < 0.01,**p-value < 0.05,**p-value < 0.10
(Robust Standard Errors)Sample 1972 - 2016 (Reduced Sample for Telecomms & CIS and Finance)
48
Table D.3: Complete Error Correction Model (Real Data)
∆ExporttCoefficients LM
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-valueEducation −0.148 0.561∗∗ 1.680 −0.366 0.76
(0.090) (0.117) (1.148) (0.253)Finance −0.356∗∗∗ 0.044 9.879∗∗ −0.079 0.66
(0.177) (0.198) (4.503) (0.894)Government −1.544∗∗ 0.493∗∗ 0.680 −0.070 0.39
(0.275) (0.198) (1.176) (0.214)Insurance & Pens−0.244∗∗ 0.380∗ −0.266 1.236 0.14
(0.084) (0.183) (1.937) (0.408)Travel −0.356∗∗ 0.188 0.263 −0.244 0.21
(0.163) (0.148) (1.031) (0.249)***p-value < 0.01,**p-value < 0.05,*p-value < 0.10
(Robust Standard Errors)Sample 1986 - 2016
Table D.4: Error Correction Model for Government Exports (RealData)
∆ExporttCoefficients LM
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-valueGovernment−1.292∗∗∗ −0.226 1.236 −0.030 0.10
0.532 0.206 1.434 0.293***p-value < 0.01,**p-value < 0.05,*p-value < 0.10
A date dummy for 2000 is included in the long run equation(Robust Standard Errors)
Sample 1986 - 2016
49
Table D.5: Complete Error Correction Model with Trend (Real Data)
∆ExporttCoefficients LM
αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-valueEducation −0.197∗∗ 0.554∗∗∗ 0.974 −0.330 0.61
(0.092) (0.114) (1.238) (0.232)Finance −0.337 0.030 9.176 −0.194 0.33
(0.199) (0.203) (4.618) (0.953)Government −1.493∗∗∗ 0.443∗∗ 0.384 −0.145 0.90
(0.289) (0.204) (1.237) (0.224)Insurance & Pens−0.233∗∗∗ 0.366∗∗ −0.687 1.204 0.14
(0.081) (0.184) (1.916) (0.408)Travel −0.317∗ 0.218 0.039 −0.317 0.06
(0.158) (0.154) (1.111) (0.237)***p-value < 0.01,**p-value < 0.05,*p-value < 0.10
(Robust Standard Errors)Sample 1986 - 2016
Table D.6: Error Correction Model with Asymmetric Exchange RateEffects (Nominal Data)
∆ExporttCoefficients LM
D∗∆ER αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-value
Aggregate 0.013 −0.055 0.331∗∗∗ 0.545 −0.114 0.25(0.329) (−0.072) (0.126) (0.157) (0.013)
Education −0.073 −0.113 0.978∗∗∗ 0.486∗∗ −0.083 0.17(0.608) (0.059) (0.212) (0.215) (0.400)
Finance −4.594∗ −0.428 0.286 1.734 2.178 0.30(2.432) (0.234) (0.154) (1.050) (1.138)
Government 0.210 −0.532∗∗ 0.165 −0.081 −0.031 0.82(0.603) (0.224) (0.167) (0.243) (0.300)
Insurance & Pens 0.112 −0.097∗∗ 0.736∗∗∗ 0.180 0.387 0.18(0.816) (0.046) (0.102) (0.291) (0.414)
Other Business 2.538∗∗ −0.284∗∗ 0.303∗∗ 0.335 −1.208∗ 0.77(1.241) (0.119) (0.140) (0.389) (0.671)
Telecomms, CIS −2.951 −0.193∗ 0.330 −1.625∗∗ 2.470∗∗ 0.15(1.172) (0.111) (0.161) (0.581) (0.876)
Tourism 0.221 −0.108 0.286 0.680∗∗∗ −0.396 0.39(0.455) (0.072) (0.174) (0.240) (0.247)
Transport 0.040 −0.195 0.463∗∗∗ 0.308 0.039 0.73(0.424) (0.137) (0.150) (0.229) (0.196)
Travel −0.167 −0.075 0.231 0.619∗∗∗ −0.317 0.02(0.438) (0.066) (0.198) (0.205) (0.228)
***p-value < 0.01, **p-value < 0.05, *p-value < 0.10(Robust Standard Errors)
Sample 1972 - 2016 (Reduced Sample for Telecomms & CIS and Finance)
50
Table D.7: Error Correction Model with Asymmetric Exchange RateEffects (Real Data)
∆ExporttCoefficients LM
D∗∆ER αX ∆Exportt−1 ∆GDPt−1 ∆TWIt−1 p-value
Education −0.073 −0.113 0.978∗∗∗ 0.486∗∗ −0.083 0.7696(0.608) (0.059) (0.212) (0.215) (0.400)
Finance −4.594∗ −0.428 0.286 1.734 2.178 0.2291(2.432) (0.234) (0.154) (1.050) (1.138)
Government 0.210 −0.532∗∗ 0.165 −0.081 −0.031 0.3837(0.603) (0.224) (0.167) (0.243) (0.300)
Insurance & Pens 0.112 −0.097∗∗ 0.736∗∗∗ 0.180 0.387 0.0182(0.816) (0.046) (0.102) (0.291) (0.414)
Travel −0.167 −0.075 0.231 0.619∗∗∗ −0.317 0.4154(0.438) (0.066) (0.198) (0.205) (0.228)
***p-value < 0.01, **p-value < 0.05, *p-value < 0.10(Robust Standard Errors)
Sample 1986 - 2016
51
Figure D.1: Fitted, Actual and Residual Plots of all Error CorrectionModels
52
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