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Acknowledgements
First and foremost, I would like to thank God for providing me with the opportunities
that I have been granted and for giving me the greatest gifts of all, my wife, Niken, and our
son, Rafi. Their unwavering love kept me strong through the difficulties of studying far away
and alone in a foreign country. I would like to thank my parents, who have taught me the
value of hard work and perseverance.
I am deeply indebted to my advisor, Professor Yoichi Okita, for his constant guidance.
Without his help, this paper would not have been completed. I would also like to give special
thank for my program director, Professor Hideo Tanaka, for continuous encouragement
during my work on this paper.
Above all, I cannot express the full depth of my gratitude to the Japan-IMF for giving
me the opportunity to study in Japan. I would also like to extend my appreciation to my home
institution, the Directorate General of Taxes of the Ministry of Finance of Indonesia for
giving me permission to study abroad.
I also would like to thank to my fellow graduate students for their advice and help,
especially Etjih Tasriah and Jassir Niti Samudro, who provided me with invaluable
suggestions and insights in the development of this paper.
Finally, I am thankful to all my friends who made my stay, at GRIPS in particular and
in Japan in general, a memorable and valuable experience.
PRATOMO Mochammad Hadi (MET06080)
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Abstract
This paper reinvestigates the validity of the Export-Led Growth (ELG) hypothesis in
Indonesia using annual data for the period 1980–2004. The conceptual model incorporates
exports into a Cobb-Douglas production function and formulates dynamic econometric
models for real gross domestic product (GDP), real exports, real gross fixed capital formation
(GFCF), and labor force. This paper employs time-series econometrics techniques to test for
the relationship and causal linkage between exports and economic growth. The obtained
econometrics results are analyzed further with disaggregate analysis. Dynamic econometrics
models are estimated to test for time-series properties: unit root test, lag length selection,
cointegration test, and Granger causality test. The result of the econometrics techniques
showed that unidirectional causality from economic growth to exports exists in Indonesia in
both the short-run and the long-run. Hence, the ELG hypothesis is not applicable in Indonesia
for the period of analysis. The results of disaggregate analysis indicated that lack of
competitiveness and strategy seem to be the shortcomings of export performance.
Keywords:
Exports, economic growth, ELG hypothesis, cointegration, Granger causality, disaggregate
analysis, export performance, Indonesia
PRATOMO Mochammad Hadi (MET06080)
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Abbreviations and Acronyms
ADF Augmented Dickey-Fuller
AFTA ASEAN Free Trade Area
AIC Akaike Information Criterion
APEC Asia-Pacific Economic Cooperation
ASEAN Association of SouthEast Asian Countries
BRICs Brazil Russia India and China
COMTRADE Common Format for Transient Data Exchange
DF Dickey-Fuller
ECM Error Correction Model
ELG Export-Led Growth
FDI Foreign Direct Investment
FPE Final Prediction Error
FTA Free Trade Area
GDP Gross Domestic Product
GFCF Gross Fixed Capital Formation
GLE Growth-Led Export
HPAEs High Performance Asian Economies
HQ Hannan-Quin
IDR Indonesia Rupiah (national currency of Indonesia)
IS Import Substitution
IT Information Technology
ITC International Trade Center
LR Likelihood Ratio
OLS Ordinary Least Square
OPEC Organization of the Petroleum-Exporting Countries
PP Phillips-Perron
SBC Schwarz-Bayesian Criterion
SIC Schwarz Information Criterion
UNSD United Nations Statistics Division
USD United States Dollar
VAR Vector Autoregressive
VAR-FD Vector Autoregressive in first-difference
VAR-L Vector Autoregressive in levels
WTO World Trade Organization
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Contents
Acknowledgements ................................................................................................................ i
Abstract ................................................................................................................................ ii
Abbreviations and Acronyms ............................................................................................... iii
Contents ............................................................................................................................... iv
1. Introduction ....................................................................................................................... 1
2. Literature Review .............................................................................................................. 4
2.1. An Overview of Economic Growth and Exports in Indonesia ..................................... 4
2.2. Previous Research on Export Led Growth Hypothesis ................................................ 7
2.2.1. Cross-Section Analysis ........................................................................................ 8
2.2.2. Time-series Analysis ............................................................................................ 9
2.2.3. Related Empirical Studies on Exports and Economic Growth for Indonesia ....... 11
3. Methodology ................................................................................................................... 12
3.1. Theoretical Model .................................................................................................... 12
3.2. Econometric Methods ............................................................................................... 15
3.2.1. Unit Roots Tests ................................................................................................ 15
3.2.2. Lag Length Selection ......................................................................................... 17
3.2.3. Cointegration and Model Residual Analysis ....................................................... 19
3.2.4. Granger-Causality Tests ..................................................................................... 21
3.3. Disaggregate Analysis .............................................................................................. 25
4. Results and Discussion .................................................................................................... 26
4.1. Descriptive Statistics Analysis .................................................................................. 26
4.2. Econometric Analysis ............................................................................................... 28
4.2.1. Unit Root Tests .................................................................................................. 28
4.2.2. Lag Length Selection ......................................................................................... 29
4.2.3. Cointegration and Model Residual Analysis ....................................................... 29
4.2.4. Granger-Causality Tests ..................................................................................... 31
4.3. Disaggregate Analysis .............................................................................................. 32
5. Conclusions and Policy Implications ............................................................................... 36
References .......................................................................................................................... 39
Appendix A: Tables ............................................................................................................ 42
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Appendix B: Figures ........................................................................................................... 51
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1. Introduction
In recent years, much research interest has been focused on the role of exports as a major
determinant of economic growth. The idea of ―export led growth hypothesis‖ (ELG) has gained
further attention since the spectacular economic success of several East Asian countries. The
economic arguments for ELG are that export expansion leads to better allocation of resources
as a consequence of international competition and higher productivity. Furthermore, export
expansion is expected to support economic development through the channel of positive
externalities spillover from exports to economic growth. Therefore many developing countries
in the world, including Indonesia, deliberately focus on boosting their export performance to
support economic growth.
The history of economic growth and exports in Indonesia is considered impressive by most
economic indicators. Indonesia‘s economy experienced an average growth rate of 7% per year
from 1967 through the mid-1990s. As a result, Indonesia‘s GDP achieved USD 202 billion in
1995, up from USD 26 billion in 1965. A similar pattern was observed on the export growth
rate. The aggregate export values increased from USD 32 billion in 1980 to USD 78 billion in
2004, with an average growth rate of 9% per year.
However, the degree to which exports bring about economic growth in economic
development of a country has been controversial in the literature. Although there have been
many empirical studies which emphasize a positive relationship between export and economic
growth, the relationship between them remains a subject of debate. Some recent studies
corroborated the existence of a causal relationship between exports and economic growth
(Ghatak, Milner, & Utkulu, 1997; Rahman & Mustafa, 1997; Mah, 2005) while others cast
some doubt on the validity of ELG hypothesis (Edwards, 1993; Shan & Tian, 1998; Richards,
2001).
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Basically, the numerous empirical studies on ELG can be classified into those based on
a cross-country data set and those based on a time-series data set (Shan & Sun, 1998).
However, with the development of time-series techniques, an increasing number of ELG
studies have sought to investigate the causality issues of different countries by employing time-
series techniques as opposed to cross-country data (i.e., Paul & Chowdhury, 1995; Ghatak et
al., 1997; Richards, 2001). The main argument against cross-country data is that the theoretical
framework used assumes a similar economic structure and production technology across
countries, which is not accurate in the cases of most countries (Shan & Sun, 1998).
Furthermore, cross-country data framework also neglects some important determinants of
growth such as fiscal, monetary, and external policies (Edwards, 1993).
Despite the abundance of ELG studies using time-series data, it should be noted that the
empirical evidence on ELG for Indonesia has been mixed. Whereas some researchers reported
results which supported ELG (i.e., Ram, 1987; Bahmani-Oskooee et al., 1991), others reported
the opposite that economic growth led exports (i.e., Rahman & Mustafa, 1997), and another
reported no correlation at all between exports and economic growth (i.e., Ahmad & Harnhirun,
1995).
Given the ambiguity of the results of previous studies, this study is useful in order to
establish further research on ELG hypothesis specifically for Indonesia. There are three main
limitations of the previous ELG hypothesis studies of Indonesia that must be identified. First,
those studies focused simply on causation between exports and economic growth without
consideration of other relevant variables that may affect economic growth. Second, they
conducted studies on too many countries, resulting in unfocused conclusions for each country
rather than one, or a few, solid conclusion(s). Finally, a deeper analysis of the econometric
results with the real performance of the analysis period‘s main variables was neglected.
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In an effort to address these gaps, the general objective of this study is to empirically
reinvestigate the ELG hypothesis for Indonesia for the period covering 1980 to 2004. These
specific objectives are to:
1. Estimate dynamic time-series econometric models on the relationship between
exports and economic growth by considering other relevant variables that affect
economic growth based on neoclassical trade theory.
2. Test for a causal relationship between exports and economic growth.
3. Conduct a disaggregate analysis on the ELG hypothesis with the real exports
performance data to obtain reliable conclusions.
To conduct this study, time-series data for the 1980–2004 periods was collected from the
World Development Indicators CD-ROM (World Bank [WB], 2006). The variables included in
the analysis were real GDP, real aggregate exports, gross fixed capital formation (GFCF), and
total labor force. All variables were deflated to 2000 constant USD, except for labor force,
which measured in unit of labor. The time-series econometrics analyses were conducted with
Eviews (version. 5.1) software.
This paper is organized into five sections. Section 1 focuses on the development of the
problem statement, justification, and objectives of the study. Section 2 describes an overview of
Indonesia‘s economic conditions and various literature reviews as background for this study.
Section 3 introduces the fundamental methodology used in this study including the selected
economic theory and econometrics methods followed in conducting the study. Section 4
presents the results and interpretations of the analysis. Section 5 provides conclusions and
implications of the analysis.
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2. Literature Review
2.1. An Overview of Economic Growth and Exports in Indonesia
Economic growth in Indonesia was once considered successful by most economic
indicators. The average economic growth rate of 7% per year and low inflation rates from 1967
to the mid-1990s, made Indonesia one of the high performance Asian economies (HPAEs)
during that time, according to a 1993 World Bank report (Krugman & Obstfeld, 2006)1.
Indonesia‘s GDP grew 7.7 times from USD 26 billion in 1965 to USD 202 billion in 1995, a
remarkable growth rate. In the 1970s, the economy grew an average of 7.8%; in the 1980s, an
average of 7%; and in 1990–1996, an average of 8% (Bresnan, 2005).
The World Bank report identified several macroeconomic conditions which made
Indonesia a part of the HPAEs. These were: conservative fiscal policies, prudent monetary
policies, flexible exchange rate management, human capital investment, high savings rates,
relatively limited price distortions, openness to foreign technology, a secure institutional
environment for private investment, pragmatic outward-looking policies, and government
intervention that encouraged rapid export growth.
Woo, Glassburner, and Nasution (1994) identified four economic sub-periods in Indonesia
between 1960 and 1990. These were the periods of: (1) guided economy (1960–1966); (2)
stabilization and rehabilitation (1967–1972); (3) oil-fueled growth spurt (1973–1981); and, (4)
external shocks (1983–1990). Each sub-period had a significant impact on Indonesia‘s
dominant export composition.
1 From a 1993 World Bank report, as cited by Krugman and Obstfeld (2006). World Bank defines HPAEs as a
group of countries which achieved spectacular economic growth 1950–1990s. Indonesia with Malaysia and
Thailand were categorized as newly industrialized economic countries (NIEC) which experienced rapid growth in
late 1970s. Unfortunately during Asian financial crisis which started in 1997, most HPAEs were severely affected by crisis.
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Rapid export growth began to rise quickly after the start of the ―New Order‖ era of the late
1960s2. Export performance began to grow in response to the rehabilitation of the traditional
export sectors during the second sub-period which resulted in strong commodity performance.
Moreover, the adoption of a realistic foreign exchange pricing policy (e.g., devaluation of the
national currency) induced exporters to sell through legal channels. During the oil boom sub-
period, as a member of Organization of the Petroleum-Exporting Countries (OPEC), Indonesia
saw a dramatic increase in oil export revenues which sustained until 1984 when the nominal oil
price started to plunge precipitously. Non-oil export commodity dominance, which was
replaced by oil during the third sub-period, got back on track in the fourth sub-period with the
manufacturing sector dominating exports composition3 (Hill, 2000).
To maintain a positive balance-of-trade and to encourage export growth, especially after
oil prices collapsed in the 1980s, the Indonesian government imposed reforms in the fiscal,
monetary, investment, and trade areas. Fiscal and monetary reform was implemented by
adjusting currency to favor exports, reactivating a stock exchange market and opening up a
bond market, deregulating the banking and finance sectors, tax reform, and tax incentives for
export commodities. Reforms in investment and trade were indicated by allowing 95% foreign
ownership in export-oriented investments, a new export credit scheme, simplification and
modernization of port administration, deregulation of export-import procedures, and tariff
adjustments (Hakim, Rachbini, Aminullah, & Pitono, 1996). These efforts supported a
macroeconomic environment that sustained Indonesia‘s high economic growth rate.
2 The rise of the New Order succeeded the previous Soekarno (1945–1966) era. As cited by Hill (2000), late in the
Soekarno era, Benjamin Higgins, the famous economist characterized Indonesia as the ―chronic dropout,‖ he
concluded that ―Indonesia must surely be accounted the number one failure among the major underdeveloped
countries‖, pp. 1. In line with Higgins statement, the chaotic situations caused by budgetary mismanagement and
related policies gravely imperiled the Indonesia‘s economy, indicated by rejection by foreign investment, declining
trade, hyperinflation (over 650 percent in 1965), and declining national income (Prawiro, 1998, pp. 6-8). All of
these factors greatly contributed to the fall of Soekarno. 3 Indonesia‘s export by groups of product (in values and percentage share) during 1985–1997 is presented in Table 8 (Appendix A) and Figure 3 (Appendix B).
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However, Indonesia‘s impressive economic performance had to end at some point, which
came during the 1997 Asian financial crisis. The crisis, which began in Thailand, contracted the
Indonesian economy by 13.6% in 1998 and made Indonesia the most seriously affected country
involved. A 15.5% decline in per capita GDP in 1998 implied that the crisis cost Indonesia 3.5
years of growth (Hill, 1999). Furthermore, the crisis had a devastating socio-economic impact.
The percentage of poor households grew from 11% in 1996 to 20% in 1999, formal wages
dropped 34% in real terms between 1997 and 1998, and the exchange rate tumbled drastically
from IDR 2,500 per 1 USD in mid-1997 to IDR 17,000 per 1 USD in January 1998. The
inflation rate soared to 57.6% in 1998, from 8.8% in 1996 (Bresnan, 2005).
The exports sector surprisingly showed a sharp decline in its major export performance
despite enormous currency depreciation during the crisis. For example, non-oil exports fell
sharply by 7.47% in 1998 and 4.33% in 1999 (Abdurohman & Zulfadin, 2002). Theoretically,
as currency depreciation occurred, it should have been followed by strong export growth
performance, such as that experienced in previous years. However, Abdurohman and Zulfadin
argued that three problems emerged due to the poor performance of exports in periods of crisis:
a sharp decline in the export commodities price due to the economic slowdown in destination
countries, highly imported components content in export commodities, and an unstable political
situation and banking crisis. In addition, the Indonesian corporate sector, as an export
generator, was seriously damaged. In 1998, 58% of leading non-financial companies booked a
net loss (Matsumoto, 2007).
To overcome the crisis‘ severe impact, a new government in Indonesia4 undertook several
measures to attempt to repair the macroeconomic situation: restructuring the banking sector, an
aggressive process of state-owned enterprise privatization, fundamental fiscal decentralization,
4 The impact of the crisis led to the fall of the Soeharto (New Order) regime. The crisis triggered the downfall
together with acute socio-economic conditions such as rampant corruption, inefficient state-owned enterprise, imprudent banking management, and problematic foreign debt.
PRATOMO Mochammad Hadi (MET06080)
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implementation of a floating exchange rate regime, and financial regulation reform (Hill, 2000).
At the start of the millennium, there were signs of economic recovery. The economic growth
average during 2000–2005 was 4.5%; 5.6% in 2006. The inflation and exchange rate were
stable and fiscal deficit had fallen to less than 2% of GDP by 2000. On trade policy, there was
further liberalization to dismantle protectionism although some significant unsupportive trade
policies remained, particularly in the agricultural sector (Hill & Shiraishi, 2007).
2.2. Previous Research on Export Led Growth Hypothesis
The idea of export expansion as a major determinant of economic growth has seen a
recurrence of interest in economic literature. From the 1960s on, in developing countries
(especially after the failure of import substitution (IS) strategy), export activities were widely
considered a path to industrialization and an instrument that useful in boosting economic
growth (Krugman & Obstfeld, 2006). Although it was assumed that export growth made a
positive contribution to economic growth, this idea has remained controversial in the literature
for the past two decades. Export expansion strategy gained popularity after the failure of IS
strategy as a suitable trade strategy for developing countries. In the 1950s and 1960s, many
Latin America and Asian countries, such as Chile, Peru, Argentina, India, and Pakistan,
followed IS strategy. By the late 1960s, African countries such as Nigeria, Ethiopia, and
Zambia began to pursue a similar strategy. However, after years of implementation, IS failed to
act as an appropriate trade development strategy. IS was eventually replaced by an outward-
looking export promotion policy similar to that which the four Asian ―tiger‖ countries adopted
(Todaro & Smith, 2006).
During the past twenty years, in accordance with export promotion strategy, numerous
empirical studies of causation of exports and economic growth have been conducted on the
economies of developing countries, using either cross-section or time series analysis.
Nevertheless, the empirical evidence has been rather mixed. While some studies supported a
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causal linkage between exports and economic growth, others failed to support the existence of a
significant relationship between these two variables (Shan & Sun, 1998).
2.2.1. Cross-Section Analysis
In the early cross-section analysis studies of this issue, the ELG hypothesis was tested by
looking at rank correlation coefficients or simple association of ordinary least square (OLS)
regressions between exports and growth and by estimating a regression equation where exports
were included as an explanatory variable in classical inputs of production. According to rank
correlation method, when the correlation coefficient between these two variables is positive and
statistically significant, then ELG hypothesis is supported. The regressions equation approach,
as a subsequent development of the rank correlation method, was conducted by estimating
output growth regression equations against exports plus a set of explanatory variables based on
neoclassical growth accounting techniques of production function5. ELG hypothesis is
supported when the coefficient of export variable is positive and statistically significant
(Ekanayake, 1999).
The major argument against cross-section analysis which employs rank correlation
coefficient was that some of the results may involve a spurious regression based on the fact that
exports were part of economic output6. Furthermore, these works also failed to consider the
possibility of other factors besides exports roles on economic output. Finally, the issue of
causality between exports and growth was not grounded on firm theoretical background
(Edwards, 1993; Ghatak et al., 1997).
Since spurious regression was a concern, the latter studies tried to incorporate a set of
explanatory variables, including exports as endogenous variables, in their regression models to
5 Giles & Williams (2000) documented more than 150 export-growth applied papers and grouped cross-section
studies into rank correlation method or simple OLS regressions between export and growth and regression
equation approach. 6 Spurious regression is regression with symptoms of having high R2, t-statistics that appear to be significant, but the results are without economic meaning (Enders, 2004).
PRATOMO Mochammad Hadi (MET06080)
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capture the causality between exports and economic growth. By conducting this procedure,
some scholars agreed there was evidence that developing countries with favorable exports
expansion tended to experience a higher rate of economic growth. However, this method had
limitations associated with the assumption that every country had similar economic conditions.
Secondly, this methodology provided little insight for dynamic behavior in a particular country
during certain periods. Finally, the assumption of no diminishing return on increased exports
share had become a major criticism (Shirazi & Abdulmanap, 2005).
2.2.2. Time-series Analysis
Considering the limitations of cross-section analysis, some studies of ELG hypothesis
applied the time-series method to test the causality between exports and economic growth7.
Among those analyses, there was a pattern of three commonly used steps: (1) test for unit roots
in the series by applying Augmented Dickey-Fuller (ADF) test and/or Phillip Perron test; (2)
test for cointegration using Johansen and/or Engle-Granger methods; and, (3) test for causality
by employing Granger approach (Sinoha-Lopete, 2006). Some of the more recent studies also
incorporated additional procedures by estimating vector autoregressive models (VAR) and/or
by testing the structural VAR.
Over the years, there have been numerous time-series studies investigating the causality
between exports and growth on the basis of neoclassical production function. The information
data set were tested in annual, quarterly, or monthly time bases. The following sections discuss
four studies that employed time-series analysis on single developing countries in Asia.
Ghatak et al. (1997) investigated the validity of ELG hypothesis for Malaysia using annual
data from 1950 to 1990 for aggregated analysis. The variables included in the analysis were real
7 The rapid development of time-series techniques made the analyses with this method more appropriate. Paul and
Chowdhury (1995), Ghatak et al., (1997), Shan and Sun (1998), Zuniga (2005), Amrinto (2006), and Lopette (2006) are a few examples of works using modern time-series analysis.
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GDP, non-export GDP, and exports. The authors tested for stationarity (ADF approach),
cointegration (Engle-Granger), and Granger causality with error correction model (ECM) for
cointegrated cases with constant. They concluded that ELG hypothesis was supported at
aggregate level for real GDP and real non-export GDP.
Mamun and Nath (2005) examined time-series evidence to investigate ELG hypothesis in
Bangladesh using quarterly data from 1976 to 2003. The authors tested for unit roots with ADF
test and lag length selected based on Akaike information criterion (AIC). The Engle-Granger
cointegration test procedure was performed to investigate the existence of cointegration
between export and economic output. ECM estimation was operated due to the existence of a
cointegrating relationship and finally the Granger causality test was employed to examine short
run causation between variables. The authors found that ELG hypothesis was supported in the
long-run; however, there was no evidence of short-run causality between export and production
function.
Mah (2005) tested the ELG hypothesis for China using annual data from 1979 to 2001.
The author tested for the existence of unit roots of real economic growth rate and export growth
rate. Optimal lag length was selected based on Schwartz criterion and Engle-Granger procedure
was employed to test for a cointegrating relationship between variables. Then, the author
performed ECM due to the existence of a cointegrating relationship. Finally, the author found
that bidirectional causality occurred between export expansion and economic growth in China.
Amrinto (2006) implemented semiparametric approach under two levels of temporal
aggregation to test the ELG hypothesis in the Philippines using annual and quarterly data from
1981 to 2004. Real GDP, real exports, real GFCF, and real effective exchange rate data were
used as variables in the analysis. Unit roots test was performed with Phillips-Perron test and
Engle-Granger procedure was operated for cointegration. Optimal lag length was determined
using Schwartz Bayesian Criterion, and subsequently after ECM was built, the author
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conducted the Granger causality test. The author found that bidirectional causality between
exports and economic growth existed in the Philippines.
2.2.3. Related Empirical Studies on Exports and Economic Growth for Indonesia
Empirical evidence on ELG hypothesis for Indonesia is mixed. Whereas some authors
reported results supporting ELG, others reported growth-led exports (GLE), and still others
reported no significant relationship between exports and economic growth8. Five articles with
empirical evidence based on time-series analysis related to growth and exports in Indonesia are
reviewed here.
Ram (1987) conducted a cross-country study using data from 88 developing countries,
including Indonesia, in various annual periods between 1960 and 1982. OLS regression method
was employed to test ELG hypothesis on variables such as the growth rates of real GDP,
population, real investment as share of output, and a dummy variable took into account the
effects of the 1973 oil crisis. The author concluded that, in Indonesia‘s case, the result was
statistically significant for export and economic growth.
Bahmani-Oskooee, Mohtad, and Shabsigh (1991) tested the ELG hypothesis for 20
developing countries using annual data within from 1951 to 1987 (Indonesia was tested from
1960 to 1985). They employed the Granger causality test with Akaike final prediction error
(FPE) to examine the variables of real GDP and export growth. The authors argued that ELG
was supported in Indonesia. In contrast, there was negative causality from economic growth to
export in Indonesia.
Ahmad and Harnhirun (1995) conducted time-series method with the variables of real per
capita GDP and exports to test ELG hypothesis in a long-run behavioral relationship in five
8 Felipe (2003) argued that the essential substance of export and growth is not only about growing by exporting,
but by exporting appropriate commodities. The main implication was that ELG strategy was not merely
competition in ‗exporting‘ which relied mainly on the developed countries as a market destination. Therefore, a prudent investigation of ELG hypothesis is indispensable in avoiding fallacy composition.
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Association of SouthEast Asian Countries (ASEAN) countries, using annual data from 1966 to
1990. They performed unit roots (ADF test) and cointegration (Johansen-Juselius procedure)
test in estimating causality between variables. ECM with constant was created in case there was
a cointegration relationship. In the case of Indonesia, the authors found that there was no long-
run relationship between exports and economic growth.
Rahman and Mustafa (1997) examined the validity of ELG hypothesis in 13 Asian
countries, including Indonesia, using annual data from 1965 to 1994. The variables included in
the model were real GDP and real export. They tested for stationarity in the series and the order
of integration using the ADF test. A cointegration test was conducted using both ADF and
Johansen-Juselius procedure; and ECM was performed to combine short-run dynamics and
long-run relationship in a unified system. Granger causality test was also conducted for the
existence of long-run relationship between variables. The authors concluded that Indonesia
experienced unidirectional causality from growth to exports both in short-run and long-run.
Ekanayake (1999) analyzed for a causal relationship between GDP and exports in eight
developing Asian countries using annual data from 1960 to 1997. He tested for unit roots in the
series with ADF test, cointegration between variables (Engle-Granger and Johansen-Juselius
procedure) and causality with Granger causality test. Optimal lag was selected with Akaike
FPE criterion and ECM was built in accordance with short-run dynamics to obtain long-run
equilibrium. The author found that, in Indonesia, bidirectional causality between economic
growth and exports occurred in the short-run and the long-run.
3. Methodology
3.1. Theoretical Model
One major reason that a country actively participates in international trade is to gain trade.
Hopefully, international trade is beneficial and has productive results for a country‘s economic
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development. As expected, trade with other countries has a positive impact for the host country,
including the ability to acquire new capital and new technologies. Furthermore, international
trade, once carried out, imposes a higher level of production efficiency and tends to allow each
country to specialize in producing its particular goods. In the other words, trade between two
countries benefits both countries if each country trades or exports the goods in which it has the
greatest comparative advantage.
The comparative advantage principle led to ELG hypothesis, a new direction for economic
policy development. ELG hypothesis postulates that export expansion is a primary determinant
of economic growth which, in turn, creates economic development. The association between
exports and growth is often attributed to the spillover effect of positive externalities on
individual countries which arise from participation in international trade, for instance, is the
efficient allocation of labor and capital and economies of scale production (Medina-Smith,
2001).
Efficient allocation, as explained in neoclassical trade theory, indicates that international
trade occurs due to the comparative advantage which caused by a relative difference in the
abundant endowment of various factors in each country (Heckscher-Ohlin theorem). Hence,
Heckscher-Ohlin theorem implies that a country produces and exports its commodities based on
intensive use of the relatively abundant factors of production or their efficient allocation
(Krugman & Obstfeld, 2006). In this theory, economies were assumed to be characterized by
constant returns to scale and perfect competition. However, international trade and
specialization was also possible resulted from the increasing returns as opposed to this theory.
According to the increasing returns theory, because of imperfect competition, trading countries
can specialize in the production of different commodities, achieving increased scale-of-
production while maintaining or increasing the diversity of available resources. Nonetheless,
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the constant return-to-scale model yields the right predictions if the countries are weak in their
economies-of-scale and differ greatly, which was the case for most countries (Krugman, 1987).
In my study, neoclassical trade theory was evaluated in a neoclassical production function
with Cobb-Douglas production function by incorporating exports into the production function.
Exports were incorporated into the production function to obtain their correlation with
aggregate output. The idea of employing Cobb-Douglas production function was plausible by
considering a two-sector growth model and following a set of assumptions: first, the economy
was composed of two sectors—one produced single tradable commodities for the world market
and the other produced non-tradable commodities for the domestic market; second, both sectors
demanded input from the economy, such as capital and labor; third, there were significant
productivity differences between the two sectors; fourth, the production of non-tradable
commodities depended on the amount of exports. Therefore, this model focused on the
likelihood of non-optimum allocation of resources due to a differential of productivity between
two sectors and where exports could include a range of positive externalities and spillovers
which are not measured by conventional national accounts (Medina-Smith, 2001).
The augmented Cobb-Douglas production function which incorporating export is specified
as follows:
Y = F (K, L, E), (1)
where Y = aggregate output (real GDP), K is capital, L is labor force, and E is aggregate real
exports of goods and services. K and L acted as direct input while E was included in the
function as a capturer of positive externalities and spillovers. The expected sign in the model
was positive for all three variables since they were all expected to have a positive effect on
output. The expectation of positive signs came from the principle that the more capital and
labor used, the higher the output. The positive sign also expected from the exports variable
PRATOMO Mochammad Hadi (MET06080)
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which derived from the premise of ELG hypothesis that the export sector yields positive
externalities.
3.2. Econometric Methods
Nowadays, economists are more interested in using time-series analysis to study the
dynamics between export expansion and economic growth among countries because of the
dynamic effect of the series. For example, the results of time-series analysis depend
substantially on condition of the analyzed countries, the period chosen, and the econometric
method used. Moreover, recent developments in time-series econometrics techniques,
especially unit-roots and cointegration techniques, have been altered to model short- and long-
term dynamics.
In this paper, four common steps of time-series analysis were followed to test for the
relationship between exports and economic growth in both the short-run and long-run. The four
steps approached in time-series studies were: (1) unit roots test (stationarity test), (2) models
specification and the lag order of integration, (3) cointegration and residual diagnostics, and (4)
Granger causality test (Gujarati, 2003). This paper followed these steps to ensure that all
variables included were stationary—either in levels or in first differences, and models and lag
order were properly specified—to observe the likelihood of long-run or short-run relationships
among integrated variables and to determine the direction of causalities between exports and
growth or vice versa.
3.2.1. Unit Roots Tests
Although a conventional model should be estimated using a system estimator or single
equation approach, it is important to consider the underlying properties of the processes that
generate time-series variables because the presence of unit roots in the series normally behave
with stochastic trends. If a series contains a unit root or is non-stationary, then the problem of
PRATOMO Mochammad Hadi (MET06080)
16
spurious regression may occur, unless it is combined with other non-stationary series‘ to form a
cointegrated stationary relationship. Essentially, the unit root test accounts for stationarity of
the series. The two most commonly used unit root tests in the literature—the Augmented
Dickey-Fuller test (ADF) and the Phillips-Perron (PP)—test were employed in this study.
The ADF test was conducted by ―augmenting‖ three equations and adding the lagged
values of the dependent variable (ΔYt). The first equation was a pure random walk equation9,
the second equation was a random walk with drift or intercept, and the last equation was a
random walk with drift around a stochastic trend. The ADF test regression is represented
below:
ΔYt = β1 + β2t + δYt-1 + αi
m
i 1
Yt-i + εt (2)
where ΔYt-1 = (Yt-1 - Yt-2), ΔYt-2 = (Yt-2 - Yt-3), et cetera, and εt is a pure ‗white noise
error term‘10
. In this equation, the main parameter was δ. If δ was not significantly different
from zero or less than critical values, the series definitely contained unit roots or was non-
stationary.
In this paper, two of the three noted equations were utilized: one with a drift or constant
(a0) and another with both a constant and a stochastic trend (a2). The null hypothesis that a0 =
δ = 0 was tested for equation with constant. The equation with both constant and trend was
tested based on null hypothesis a2 = δ = 0. The test either failed to reject null hypotheses for
selected series, then series contains unit roots, or it implied that the series in levels were non-
stationary and must be modeled in first differences (I(1)), or were stationary. Otherwise, if
9Pure random walk is a stochastic trend that in a time-series is changing over time in an unpredictable behavior. 10 ‗White noise error term‘ happens when the value of disturbance term in period t is equal to ρ times its value in
previous period plus a purely random error term. In other words, white noise happens if the values in the sequence
existed without serial correlation, has mean of zero (E(εt) = 0), and var (εt) = ζ2.
PRATOMO Mochammad Hadi (MET06080)
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calculated t-statistics were greater than critical values then the series was stationary and must be
modeled in levels (I(0)).
Phillips and Perron (1988) generalized and modified DF test procedure. PP test used
nonparametric statistical methods for serial correlation in the error term without adding lagged
difference terms. On the other hand, the DF test accounted for possible serial correlation in the
error term by adding the lagged difference terms of the regressand (Gujarati, 2003).
Furthermore, PP test assumed that the expected value of the error term was equal to zero,
however PP tests did not require the error term be serially uncorrelated. PP versions of the DF
test were flexible in terms of serial correlation between disturbances that can be an
autoregressive or moving average form (Patterson, 2000). Both the PP test and ADF test used
similar critical values.
3.2.2. Lag Length Selection
A critical factor in the specification of appropriate VAR models is the selection of the lag
length. There are several criteria recommended for the most appropriate VAR model (Yang,
2002). Some of the criteria are the likelihood ratio test (LR), final prediction error (FPE), the
Akaike information criterion (AIC), the Schwarz information criterion (SIC), and the Hannan-
Quin information criterion (HQ). In all alternatives, the model that best fits the data is the one
that minimizes the overall sum of squared residuals or maximizes the likelihood ratio.
Therefore, this study used AIC since this criteria contributed to the trade off of a reduction in
the sum of squared residuals to form a more parsimonious model.
Accordingly, two types of bivariate VAR models11
were developed—1) VAR models with
only two endogenous variables (GDP and exports); and 2) VARX models with both two
endogenous variables (GDP and exports) and two exogenous variables (GFCF and labor).
11 The models called bivariate because of the number of dependent/endogenous variables in the VAR models.
PRATOMO Mochammad Hadi (MET06080)
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The reasoning for developing VAR models without exogenous variables was because of
their simplicity. Also, they are commonly used in applied studies related to ELG hypothesis
(Rahman & Mustafa, 1997). In this case, the previous studies on the ELG hypothesis applied
were based on a pair analysis of causality simply between GDP and exports.
The development of bivariate models which incorporate exogenous factors such as capital
and labor is based on neoclassical trade theory assumption. This theory has treated capital and
labor as inputs of production. Therefore, VAR models with current exogenous variables were
introduced into the analysis because the exogenous variables (capital and labor) should be
treated as inseparable factors in the production system (related with GDP and exports).
The bivariate models incorporating exogenous variables were considered a new dimension
of ELG analysis and trade literature because previous studies rarely introduced the exogenous
variables into an equation when testing the ELG hypothesis. Previous work that incorporated
bivariate models within the equation was done by Sinoha-Lopete (2006). My study followed
steps required to develop such a model. In addition, by developing bivariate models with
exogenous variables, the comparison with bivariate models without exogenous variables was
viable. Bivariate models with exogenous variables were also introduced to reduce problems of
possible multi-co-linearity in the data selection. Both bivariate VAR models are written
compactly as:
yt = β0 + β1yt-1 + β2yt-2 + .. + βiyt-p + εt (3)
where yt is a vector containing the variables (GDPt, EXPt, GFCFt, LABt), each of βi represents
a coefficient and εt represents the Gaussian white noise error which was assumed to be
uncorrelated.
PRATOMO Mochammad Hadi (MET06080)
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3.2.3. Cointegration and Model Residual Analysis
Engle and Granger (1987) developed the cointegration test method to overcome non-
stationary time-series due to unit roots inherent problem. They found that a linear combination
of two or more non-stationary series may be stationary, so that, if this stationary linear
combination exists then the non-stationary time-series are said to be cointegrated. Thus, the
stationary linear combination may be interpreted as a long-run equilibrium relationship among
the variables.
The concept of this long-run relationship was extended by Johansen (1988). He created a
procedure based on developing generalized models that allow for a higher order of
autoregressive processes, such as in ADF tests. In brief, Johansen‘s procedure is as follows:
For a particular vector autoregressive (VAR) of order of p:
yt = A1yt − 1 + … + Apyt−I + Bxt + εt (4)
where yt is a k-vector of non-stationary I(1) variables, xt is a d-vector of deterministic
variables, and εt is independent and identically distributed n-dimensional vector. Then, this
VAR could be rewritten as,
Δyt = Πyt-1 +
1
1
p
i
Γi Δyt−i + Bxt + εt (5)
where
Π =
p
i 1
Ai – I and Γi = -
p
ij 1
Aj (6)
and Π is the long-term matrix containing information whether the condition of yt is either
cointegrated or not-cointegrated and Γi is the number of cointegrating relationships
(cointegrating rank). This study implements Johansen‘s cointegration procedure to test for the
PRATOMO Mochammad Hadi (MET06080)
20
possibility of at least one cointegrating relationship between GDP and exports in all bivariate
models developed for Indonesia including the trace and maximum eigenvalue tests.
The trace test attempts to determine the number of cointegrating vectors between the
variables by testing the null hypothesis that r = 0 against the alternative that r > 0 or r ≤ 1 where
r is equal with the number of cointegrating vectors. The maximum eigenvalue tests the null
hypothesis that the number of cointegrating vectors is equal to r against the alternative of r + 1
cointegrating vectors. Thus, if the value of the LR was greater than the critical values, the null
hypothesis of zero cointegrating vectors was rejected.
The econometric model of this relationship captures several bivariate (GDP and exports)
models without exogenous variables and with exogenous variables, all expressed in logarithmic
form. The econometric model of the relationship between the variables in both types of
bivariate models is based on augmented neoclassical trade theory, where all variables are
expected to have a positive effect on aggregate output. Therefore, in this analysis, in the
bivariate models with exogenous variables, GFCF and labor were treated as exogenous
variables. Below is a representation of an econometric model of the bivariate form in this study
with exogenous variables:
lnYt = β0 + β1 ln EXPt + β2 lnGFCFt + β3 ln LABt + εt , (7)
where Y is aggregate output (real GDP), EXP is total real exports of goods and services, GFCF
is real gross fixed capital formation, LAB is labor force, and εt is the stochastic disturbance
term (error terms). Econometric theory assumes that the residual sequences in both types of
bivariate models are stationary, as the result, the linear combination of non-stationary series
will be stationary as well.
PRATOMO Mochammad Hadi (MET06080)
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Model residual analysis was conducted by applying the Ljung-Box residual autocorrelation
test and Jarque-Bera normality test12
. The aims of both tests were to ensure that the selected lag
lengths were the best fit for the selected VAR models, so that the residuals of the designed
models were uncorrelated (white noise) and normally distributed. Ljung-Box test verified that
residuals were uncorrelated up to some predetermined number of lags. The latter test examined
that the residuals were normally distributed.
3.2.4. Granger-Causality Tests
Granger causality has been extensively used in empirical economics related to ELG
models as mentioned in subsections 2.2.2 and 2.2.3. This study tested for the direction of
causalities between GDP and exports in the final stage of analysis as well. Specifically, it tested
covering bivariate models without exogenous variables and bivariate models with exogenous
variables. To test for causality between GDP and exports, three Granger causality alternative
models were stipulated on both types of bivariate models: VAR in levels, VAR in first
differences, and the error correction model (ECM).
The VAR in Level model assumes the series are to be integrated of order zero
(stationary)—I(0) in levels. When the unit roots test indicates that series in levels are stationary,
they can be modeled as VAR-L. If the series of this study are stationary in levels, various VAR-
L are developed for both types of bivariate models. These models were used to test for
Granger-causality between GDP and exports. The models tested for VAR-L with exogenous
variables are specified as follows:
lnYt = b10 +
p
i 1
ϕ1i lnYt-i +
p
j 1
b1j lnEXPt-j +
p
k 1
λ1k 1nGFCFt-k +
p
l 1
ρ1l lnLABt-l + εt1 (8)
12 If the model defects at the checking stage, for example, if residual autocorrelation was found, this is an
indication of representation of poor models in data generation process. Improvements are then made by adding
other variables or lags to the model by including nonlinear terms or changing the functional form (Lutkepohl, 2004).
PRATOMO Mochammad Hadi (MET06080)
22
ln EXPt = b20 +
p
i 1
b2i lnEXPt-i +
p
j 1
ϕ2j lnYt-j +
p
k 1
λ2k lnGFCFn +
p
l 1
ρ2m lnLABt-l +εt2, (9)
where φ, b, λ, and ρ represent the coefficients of the variables; and εt1 and εt2 are random
disturbances with mean zero, without serial correlation and stationary. The lag length orders of
the variables are p as an autoregressive process and b as exogenous variables.
The null hypotheses (H0) state that, exports do not Granger-cause economic growth and
economic growth does not Granger-cause growth in export. Based on equations (8) and (9), the
joint hypotheses for Granger non-causality between GDP and exports, is specified as follows:
From EXP → GDP (for equation 8),
If H0: b11 = b12 =… b1p = 0,
From GDP → EXP (for equation 9),
If H0: φ21 = φ 22 =… φ2p = 0.
The VAR model in first differences (VAR-FD) was used if results of unit root test
indicated that the variables were integrated of order one—I(1) but not cointegrated. When this
was the case, Granger-causality was tested to estimate VAR-FD for both types of bivariate
models. Below is the representation of GDP and exports in VAR-FD model with exogenous
variables:
ΔlnYt =b10+
p
i 1
ϕ1iΔlnYt-i+
p
j 1
b1jΔlnEXPt- j+
p
k 1
λ1kΔ1nGFCFt-k +
p
l 1
ρ1lΔlnLABt-l + εt1 (10)
Δln EXPt =b20+
p
i 1
b2iΔlnEXPt-i+
p
j 1
ϕ2jΔlnYt-j+
p
k 1
λ2kΔlnGFCFn+
p
l 1
ρ2mΔlnLABt-l+εt2 , (11)
where Δ is the first difference operator.
PRATOMO Mochammad Hadi (MET06080)
23
The null hypotheses (H0) state that, exports do not Granger-cause economic growth and
economic growth does not Granger-cause growth in export. Based on equations (10) and (11),
the joint hypothesis for Granger non-causality between GDP and exports based on no
cointegrating relationships is specified as follows:
From EXP →GDP (for equation 10),
If H0: b11 = b12 =… b1p = 0,
From GDP → EXP (for equation 11),
If H0: φ21 = φ 22 =… φ2p = 0.
The error correction model (ECM) applies to nonstationary series that are known to be
cointegrated. When this is the case, ECM should be applied because it has cointegration
relations built into the specification so that it restricts the long-run behavior of the endogenous
variables. The ECM is used to estimate the significance of the error term in the cointegrating
vectors to see if long-run equilibrium will gradually achieve and contemporaneous changes in
the variables that determine equilibrium are adjusted (Patterson, 2000). In sum, ECM has both
long-run and short-run properties built in. The long-run properties are embedded in error term
while the latter is partially captured by the equilibrium error term (Koop, 2000).
If the series in this study were nonstationary and cointegrated, then cointegrating equations
was extended by incorporating the error term into the models and testing for the significance of
the adjustment coefficients in each of the cointegrating equations. Below is an illustration of
GDP and exports equations that fit the ECMs in the bivariate model without exogenous
variables (equation 12 and 13) and the bivariate model with exogenous variables (equation 14
and 15):
PRATOMO Mochammad Hadi (MET06080)
24
ΔlnYt = b10 +
p
i 1
ϕ1iΔlnYt-I +
p
j 1
b1jΔlnEXPt- j - χZt-1 + εt1 , (12)
Δln EXPt = b20 +
p
i 1
b2i ΔlnEXPt-I +
p
j 1
ϕ2jΔlnYt-j - χZt-1 + εt1 , (13)
ΔlnYt=b10+
p
i 1
ϕ1iΔlnYt-i+
p
j 1
b1jΔlnEXPt- j+
p
k 1
λ1kΔ1nGFCFt-k+
p
l 1
ρ1lΔlnLABt-l -χZt-1+εt1, (14)
ΔlnEXPt=b20+
p
i 1
b2iΔlnEXPt-i+
p
j 1
ϕ2jΔlnYt-j+
p
k 1
λ2kΔlnGFCFn+
p
l 1
ρ2mΔlnLABt-l-ζZt-1+εt2, (15)
where χ and ζ are long-run adjustment parameters and Zt-1 is the error correction term
representing lagged residuals from cointegrating relationship.
From the previous four equations (12, 13, 14 and 15), the joint hypotheses for Granger
non-causality based on first difference I(1) and cointegrated equations for both types of
bivariate models is specified as follows:
There is no Short-Run Causality: The null hypotheses state that, in the short-run, exports do not
Granger-cause GDP and GDP does not Granger-cause exports.
From EXP → GDP,
If H0: b11 = b12 =… b1p = 0,
From GDP → EXP,
If H0: φ21 = φ 22 = … φ2p = 0.
There is no Long-Run Causality: The null hypotheses state that, in the long-run, exports do not
Granger-cause GDP and GDP does not Granger-cause exports.
PRATOMO Mochammad Hadi (MET06080)
25
From EXP → GDP,
If H0: χ = 0,
From GDP → EXP,
If H0: δ = 0.
There is no Total Causality: The overall null hypotheses state that, exports do not Granger-
cause GDP and GDP does not Granger-cause export.
From EXP → GDP,
If H0: b11 = b12 =… b1p = χ = 0,
From GDP → EXP,
If H0: φ21 = φ 22 =… φ2p = δ = 0.
In all error correction models, failure to reject the null hypotheses indicates that the exports
led growth (ELG) and GLE hypothesis are not valid.
3.3. Disaggregate Analysis
The econometrics methods provide a basis of empirical evidence to produce an analysis
based on the obtained results. Yet, the necessity in bridging with practical implementation is
important. Therefore, in section 4.3, the result of the econometrics methods in accordance with
ELG hypothesis testing is analyzed further with the Indonesia‘s actual exports performance in
various years based on the data obtained from World Trade Organization (WTO) and
International Trade Center (ITC). The disaggregate analysis of empirical evidence and real
performance of exports is plausibly significant to obtain unambiguous picture of exports‘
condition in Indonesia.
PRATOMO Mochammad Hadi (MET06080)
26
4. Results and Discussion
This section presents the results of the ELG hypothesis investigation of Indonesia for the
period 1980–2004. It consists of three sub-sections: Sub-section 4.1 describes economic
performance related to the examined variables of the analyzed periods; Sub-section 4.2 portrays
the results of the econometrics analysis (unit roots, cointegration and Granger causality test)
used to test connecting linkages between economic growth and exports; and, sub-section 4.3
discusses a comparative analysis of the ELG hypothesis and the findings in accordance with
macroeconomic perspective.
4.1. Descriptive Statistics Analysis
Four macroeconomic indicators (real GDP, real exports, real GFCF, and labor force) were
analyzed in this study to describe Indonesia‘s economic performance. As shown in Figure 1
(Appendix B), since 1980, all four macroeconomic indicators trended upward. However, the
impact of Asian financial crisis in 1997 resulted in a declining trend for GDP, exports, and
GFCF during certain periods.
GDP increased rapidly from 1980 until 1997. The average 7% per year economic growth
rate caused significant changes in GDP value. However, in 1998, the crisis contracted GDP by
13.6%. Subsequently, economic growth started to rise again in 1999, although the rate of
economic growth was not as spectacular as before the crisis.
In Indonesia in the early 1980s, the export rate was not stable due to the oil price drop at
the time. The value of exports, which relied mainly on oil exports, was also affected. Signs of
change began to appear in 1985 after the government reformed several policies including the
fiscal, monetary, and trade procedures that diversified exports so that they were not so highly
dependent on oil. The years between 1980 and 1985 were transitional for the exports sector,
which moved from oil reliance to more diversified commodities. Unfortunately, the disastrous
PRATOMO Mochammad Hadi (MET06080)
27
1997 financial crisis had a huge impact on exports. In 1999, the value of exports plunged more
than 31% compared with the previous year. The unstable macroeconomic condition and
significant losses of exports value were suspected as major causes in the decline of exports.
However, beginning in 1999, the exports sector started to rise again with average growth of 8%
per year.
GFCF had a strong relationship with foreign direct investment (FDI). Since the
government reformed investments regulations in the mid-1980s and gradually improved the
investment climate, FDI, as reflected in GFCF, showed acceleration in investment value from
USD 13.8 billion in 1984 to USD 51.2 billion in 1997. In other words, there was an average
growth rate of 9.8% per year from 1984 to 1997. However, the financial crisis hurt the
investment sector with a 45.2% fall in 1999 before the sector started to regain steadily in 2000.
The labor force in Indonesia increased at a steady rate. On average, the labor force growth
rate was 2.6% per year from 1980 to 2004. The relationships among GDP, exports, GFCF, and
labor force are discussed in the following paragraph in terms of correlation analysis.
A correlation matrix analysis (Table 2, Appendix A) was performed to investigate the
correlation between variables (real GDP, real exports, real GFCF, and labor force). The results
showed significant and positive correlation among variables (on average, more than 90% of
each variable). However, strong correlation does not imply causation from one variable to
another (e.g., causation from exports to GDP or vice versa).
Therefore in the following section, this study examined the three methods commonly used
in time-series analysis in accordance with validity of ELG hypothesis. Econometrics techniques
such as stationarity test, cointegration test, and Granger causality test was employed to find
short-run relationship, long-run relationship, and direction of causation between exports and
economic growth.
PRATOMO Mochammad Hadi (MET06080)
28
4.2. Econometric Analysis
4.2.1. Unit Root Tests
Unit root tests were conducted first, with real GDP, exports of goods and services, GFCF,
and labor as the time-series variables considered in this study. These variables must be
stationary or cointegrated in order to avoid a spurious regression situation and to ensure
whether they are stationary or not.
ADF and PP test were conducted with critical values 10% applied for both test. The results
of both tests are summarized in Table 1 (Appendix A). This table is the result of the unit root
test for Indonesia‘s annual data (1980–2004) on all four series (GDP, exports, GFCF, and
labor). The distribution of the table is as follows: Column 1 represents the equations used with
a constant and both constant and a trend (ADF and PP test); Column 2 captures the series in
logarithmic form and the optimum lags in level for ADF test; Column 3 shows the null
hypothesis in levels and in first difference for ADF and PP test; Columns 4 through 7
summarize the t-statistics and decision to accept or reject the null hypothesis in levels
condition. The second part of Table 1 (columns 8 through 12) summarizes the ADF and PP
tests in first differences and the decision of accepting or rejecting the null-hypothesis. Both in
levels and in first differences, the optimum lags were acquired using AIC.
For Indonesia, the ADF and PP test results indicated that all of the series are non-
stationary in levels or the results failed to reject the null-hypothesis except for variable exports
with constant trend in lag 0. This finding of nonstationary series is consistent with previous
literature that demonstrated for most of macroeconomics series is expected to contain unit root.
Thus, to correct for the presence of unit root in all series, first differences measures were
taken. The result of the unit roots tests in first difference based on ADF tests and PP tests
showed that, for the most part, GDP, exports, GFCF, and labor were stationary in their first
PRATOMO Mochammad Hadi (MET06080)
29
difference. Therefore, all variables were found to be integrated in order 1 in the models with
trend or without trend.
4.2.2. Lag Length Selection
There are several statistical criteria which might be used to determine the optimal number
of lags for a VAR in levels. There are the AIC, the Schwartz-Bayesian Criterion (SBC), and the
LR test. This study employed AIC due to the limited sample size range used to estimate the
appropriate number of lags entering the VAR of two types of bivariate models (VAR (p) and
VARX (b) model).
The result of lag length selection showed that the optimum number of lags in the specified
VAR (p) model was 3 when the AIC is minimum (-6.683711). For VARX (b), the AIC showed
that VARX model was optimum at 1 when AIC is minimum (-7.114560). Hence, the AIC
identified the optimum number of lags on a VAR model of order 3 and 1 for VAR (p) and
VARX (b) respectively. These models were used to test for cointegration between economic
growth (real GDP) and real exports in Indonesia.
4.2.3. Cointegration and Model Residual Analysis
Having confirmed unit roots presence in all data series and the findings of stationarity in
first difference, the next step was a two-step Engle-Granger cointegration procedure. The first
procedure was to determine the order of integration and long-run equilibrium relationship (if
the series are stationary in first difference then the long-run relationship among variables needs
to be estimated13
). The second procedure was to specify ECM to account for short-run
equilibrium if the variables are cointegrated or stationer in first difference- I(1).
13 Engle-Granger suggests regressing each variable against all other variable in order to estimate long-run equilibrium.
PRATOMO Mochammad Hadi (MET06080)
30
Johansen‘s cointegration test was employed for both VAR (p) and VARX (b) model to
find long-run equilibrium between variables. In other words, to determine if the variable were
related to each other in the long-run. The assumption behind the cointegration test that
performed in this study was that there is a deterministic trend in that the data allows for
appropriate ECM estimation in a cointegrating equation.
Trace and maximum eigenvalue tests were applied to determine the rank of cointegration.
For the trace test, the critical value at 5% was 15.49471 at r = 0 and 3.841466 at r =1. For
maximum eigenvalue test, the critical value at 5% was 14.26460 at r = 0 and 3.841466 at r = 1.
If both tests‘ results indicated larger value than critical value then null hypothesis was rejected,
and failed to reject null hypothesis if otherwise. Table 3 (Appendix A) demonstrated the
summary of Johansen‘s cointegration test, for both the cointegration test on the bivariate model
without exogenous variable and cointegration test on the bivariate model with exogenous
variables. Column 1 represents the bivariate model that was used. Column 2 corresponds to the
null hypothesis of both trace and maximum eigenvalue test. Column 3 and 4 indicate the
computed values of trace and maximum eigenvalue. The last column represents the rank of
cointegration.
Overall, there was one cointegrating relationship between real GDP and real exports for
the bivariate model without exogenous variable. And there were two cointegrating relationships
between real GDP and real exports for the bivariate model with exogenous variables. Based on
this test, economic growth and its selected determinants exhibited a long-run relationship. This
means that real GDP, exports, GFCF, and labor tend to move jointly over the entire period of
analysis.
Residual analysis of the models was conducted for adequacy of selected models with
respect to autocorrelation and normality. The tests used were the Ljung-Box test for
autocorrelation and the Jarque-Bera test for normality. The null hypothesis for autocorrelation
PRATOMO Mochammad Hadi (MET06080)
31
test showed that there was no autocorrelation up to the lag specified and the null hypothesis for
normality test was based on the assumption that the residual were normally distributed. The
rejection of the normality test might indicate heterocedasticity in the model.
The results for both models (bivariate model without exogenous variable and bivariate
model with exogenous variables) are presented in Table 4 (Appendix A). The autocorrelation
test results indicated that there was no presence of autocorrelation for either model except for
lag four in the bivariate model without exogenous variable. Normality test results showed that
for the bivariate model without exogenous variable, the residuals were not normally distributed.
On the other hand, the normality test results did indicate for residuals normal distribution in the
bivariate model with exogenous variables.
4.2.4. Granger-Causality Tests
The cointegration test results showed that two of three alternative VAR models proposed
were applicable in this study14
. There were VAR-L (causality with full rank variable) and ECM
(causality with cointegrated series). Therefore, the Pairwise Granger causality test was
performed in search of a direction of causation between real GDP and exports. Similar lag
length in the specified VAR (p) and VARX (b) models were used to estimate VAR-L and
ECM.
Tables 5 and 6 (Appendix A) demonstrate the summary of the Pairwise Granger Causality
test of ELG and GLE for both types of bivariate models. The decision to reject the null
hypothesis of no causality between variables was transformed into a conclusion of YES in the
last column of the table and a conclusion of NO otherwise. Table 5 shows that at 5% critical
value, the results of the causality test showed that exports do not cause real GDP, implying the
14 There are three proposed models in the methodology section prepared to anticipate the results of cointegration
test. As mentioned in section 3.2.4., VAR in levels (VAR-L), VAR in first difference (VAR-FD), and ECM models are the three proposed models.
PRATOMO Mochammad Hadi (MET06080)
32
acceptance of ELG hypothesis was not valid. However, the causality test results did indicate
that there was strong causation from GDP to exports. In general, with full rank bivariate models
with exogenous variables, unidirectional causality was found from GDP to exports. Therefore,
it was concluded that for this model, only GLE was valid for Indonesia.
According to the results of the cointegration test, the ECM was applicable for the bivariate
model without exogenous variables. A vector ECM is a restricted VAR designed for
cointegrated nonstationary series in levels. The ECM has a cointegration relationship built in so
that it restricts long-run behavior of endogenous variables to converge on their cointegrating
relationship in accordance with short-run adjustment dynamics. The term ‗cointegration‘ also
known as the error correction term since the deviation from long-run relationship is adjusted
gradually via a series of partial short-run adjustments. Therefore, the significance of error
correction term was tested to determine the convergence speed of GDP and exports in bivariate
model without exogenous variable.
The ECM test results (Table 6, Appendix A) indicated: Firstly, for the model with
exogenous variable no causation was found from exports to GDP, but, on the contrary, there
was strong causation from GDP to exports. Secondly, the evidence for the model without
exogenous variables indicated that both in the short-run and the long run that there were no
causations found from exports to GDP. In other words, for both short-run and long-run, there
was no evidence found to support the ELG hypothesis in Indonesia. In contrast, there was
strong causation in short-run and long-run from GDP to exports. Hence, at a 5% critical value,
there was a unidirectional causality that existed from GDP to exports in Indonesia.
4.3. Disaggregate Analysis
ELG hypothesis was investigated in Indonesia from 1980 to 2004. Two types of bivariate
models (VAR and VARX) were estimated and the direction of causation between growth and
export were examined both in the short-run and the long-run as well. The results of causality
PRATOMO Mochammad Hadi (MET06080)
33
indicate that growth rate has contributed to the exports sector. This finding implied that
economic growth in Indonesia was driven mostly by factors other than exports. Furthermore,
this finding was corroborated by Rahman and Mustafa‘s (1997) conclusion that Indonesia
experienced unidirectional causality from growth to exports both in the short-run and the long-
run.
The findings of GLE have broad spectrum implications. Firstly, the rate of export growth
relies on the economic growth rate. Hence, government should place more emphasis on
developing higher economic growth rate policies to spur exports. Secondly, the use of exports
as major determinants of economic growth as stated in ELG hypothesis might encounter
problems in practice. Perhaps, the problems may be attributed to the inadequate
competitiveness, inaccurate strategies, and ineffective execution of export policies
The competitiveness of the exports commodities sector should be questioned. Indonesia‘s
trade performance index in 2003 (Table 9, Appendix A) showed that for 12 export sectors,
there were merely two sectors (wood products and clothing) which achieved a top ten position
in the world market. Moreover, for both sectors, their share in the world export market was
merely less than 3% implied that the exporter of wood products and clothing was not being
dominant player in world market. In addition, other export sectors (electronic component, IT
and consumer electronics, miscellaneous manufacturing, chemicals, basic manufactures,
transport equipment, and non-electronic machinery) also had inadequate performances which
reflected their share less than 1% share in the world market.
Another issue related to export competitiveness was comparative advantage. As mentioned
in section 3.1, comparative advantage is obtained by intensive use or efficient allocation of
relatively abundant endowment factors. Table 11 (Appendix A) represents a specialization
index describing the degree of comparative advantage of Indonesia‘s group of commodities for
2003. From the table description, only wood products successfully attained more than a three
PRATOMO Mochammad Hadi (MET06080)
34
point level for comparative advantage. By contrast, several commodities (IT and consumer
electronics, electronics components, miscellaneous manufacturing, chemicals, non-electronics
machinery, basic manufactures, and transport equipment) were miserably stuck below one point
level. The indicator of comparative advantage in Table 10 inferred that efficient allocation was
underachieved despite Indonesia‘s relative abundance of endowment factors.
In the wider perspective of competitiveness, Indonesia‘s exports performance was related
to the achievement of exports. Table 12 (Appendix A) ranks exports performance based on
various categories. In general, in terms of ranking in net exports, in the manufacturing exports
categories, there were four sectors (basic manufacturing, chemicals, non-electronic machinery
and transport equipment) under performed in records. Especially for the basic manufacturing
sector, there was a sharp decline in achievement from ranking 33 in 2001 to ranking 102 in
2005. In case of product diversification, there were only two sectors (clothing and IT and
consumer electronics) that achieved a top ten position. However, despite this product
diversification in the clothing sector, it still lagged behind in terms of an adaptation effect and
matching with dynamic world demand. It means that for the clothing sector, the effort of
product diversification was not paired with adaptation to rapidly changing world demand.
Deeper concern for the value of export commodities must be seriously considered. As
shown in the category of relative unit value (Table 12, Appendix A), all export products from
Indonesia had relatively low values. Meaning, the export products had low added value
contents built into the commodities. Secondly, it means that there was no significant
improvement in product quality.
The issue of export competitiveness has become more important in recent years. The world
globalization trend is signed with the increased popularity of free trade areas around the
PRATOMO Mochammad Hadi (MET06080)
35
world15
. Regionally, free trade area establishment in Southeast Asia dates back to 199216
. In the
broader region, the Asia-Pacific Economic Cooperation (APEC) organization was established
in 198917
. Despite its purpose of further enhancing economic growth and prosperity for its
members, the establishment of these organizations has consequences in higher competitiveness
demands for commodities of export.
One issue regarding the ineffective export policy is market destination. Indonesia‘s
conventional export market destinations were United States and Japan. As shown in Table 10
(Appendix A), although the proportion of export share in both countries declined from 67.9% in
1985 to 36.9% in 2001, they accounted for more than one third of aggregate exports. High
dependence on conventional markets posed a potential hindrance to market expansion. The
findings in Table 12 (Appendix A) also support high dependence on the conventional market.
In case of market diversification ranking, most of the exports sectors were concentrated on the
existed market resulting in low ranking level for market diversification. With the rise of new
emerging markets (i.e., China, Russia, South Asia, Latin America, and Western-Southern
Africa), there are possibilities to explore this new prospective market18
.
Inaccuracy of exports strategic development was underpinned by shortcomings in
predicting the world exports pattern. As shown in Table 7 (Appendix A), from the 1980s, the
growth rate of agricultural products and mining in the world had relatively a small increase
compared with manufactured products. Unfortunately, from 1988 to 2005 the exports
composition from Indonesia relied heavily on mining products which on average accounted for
31% share of total exports (Figure 2, Appendix B). The major disadvantage of focusing on
15 Free Trade Area is an agreement at a bilateral or multilateral level, in which each country‘s commodities can be
shipped to the other(s) without tariffs; however this country may set tariffs against the rest of the world (Krugman
and Obstfeld, 2006). 16
One purpose of ASEAN-Free Trade Area (AFTA) establishment is to eliminate tariff barriers among ASEAN
members with the goal of regional economic integration. 17 Similarly with AFTA, APEC works to reduce tariffs and other trade barriers across the Asia-Pacific region. 18 As pointed out by the Goldman Sachs investment bank, they argued that the economies of ‗BRICs‘ (Brazil,
Russia, India, and China) are rapidly developing and by the year 2050 they will surpass most of the current richest countries. On almost every scale, these countries will be the largest entity on the global stage by 2050.
PRATOMO Mochammad Hadi (MET06080)
36
mineral products referred to non-renewable resource characteristics which mean that at some
point they will be depleted.
However, according to Table 2 (Appendix A), positive and significant correlation between
GDP and exports was found in Indonesia. This finding indicated positive spillover externalities
between exports and growth. Although there lacked current export strategies, the crucial role of
exports in economic growth is indubitable.
5. Conclusions and Policy Implications
This paper reinvestigated the ELG hypothesis for Indonesia using annual data for the
period 1980–2004. Time-series techniques such as unit root tests (ADF and PP tests),
cointegration test (Johansen‘s procedure), and Granger causality tests were applied to test the
causal relationship between exports and economic growth. This paper contributes to the
existing literature on ELG hypothesis—especially on studies of ELG hypothesis for
Indonesia—by implementing comparative analysis with duo models (model with exogenous
variables and without exogenous variables) which had been used rarely in empirical ELG
hypothesis studies. In addition, disaggregate analysis with actual export performance as a
counterpart of econometric analysis was regarded as strengthening measures of econometrics
results. Below is the summary of major findings of econometrics analysis and disaggregate
analysis.
The unit roots test result from ADF and PP tests indicated that all of the series is non-
stationary in levels and subsequent tests in first difference showed that most of the variables
were stationary. Johansen‘s cointegration test demonstrated that for both models, long-run
relationships existed between GDP and exports. There were cointegrating relationships for the
model without exogenous variable and for the model with exogenous variables. The Granger
causality test indicated that direction of causation in the long-run occurred from GDP to exports
PRATOMO Mochammad Hadi (MET06080)
37
for the model with exogenous variables. Parallel results were also obtained from the model
without exogenous variable that direction of causation existed from GDP to exports both in the
short-run and long-run. Therefore, based on these results, there was no evidence to support the
ELG hypothesis in Indonesia. In contrast, there was GLE that existed as reflected in the
unidirectional causality from growth to exports.
This finding was corroborated by the conclusions of previous studies (Rahman & Mustafa,
1997) that Indonesia experienced unidirectional causality from growth to exports both in the
short-run and long-run. The result of the econometrics test implied that export growth was
driven mainly by economic growth. Hence, policy should place more emphasis on higher
economic growth to spur exports. Secondly, the idea of exports as major determinants of
economic growth might have problems in running exports policies.
As discussed in sub-section 4.3, there are at least three major problems related to the
exports situation in Indonesia. These problems are: inadequate competitiveness, inaccurate
strategy, and ineffective execution of export policies. Therefore, three important policy
implications can be drawn from this analysis to deal with unsatisfactory exports performance.
First, in order to improve Indonesia‘s exports competitiveness, the government should lift
the barriers that restrict the creation of a conducive business climate. Consequently,
deregulation and elimination of ‗the high cost economy‘ is highly recommended. Furthermore,
the availability of adequate infrastructure is undoubtedly needed to support the efficient
distribution and effective production of export commodities.
Secondly, prioritizing high value added manufactured exports commodities seems to be an
effective way to boost exports performance since the world demand for manufactured products
is higher than agricultural and mineral products. In addition, by making high value added
manufactured commodities a priority, there are larger multiplier effects on the macroeconomic
environment. Furthermore, it will strengthen the competitiveness levels of aggregate exports by
PRATOMO Mochammad Hadi (MET06080)
38
reducing the dependence on primary export products because the price of primary products
tends to be highly fluctuating. Therefore, government should address this priority by providing
special incentives for some targeted high value added manufactured industries. The incentives
may be in the form of tax holidays (especially for initial establishment), full support for
research and development, and tariff reduction (on some specific imported raw and
intermediate materials and machinery).
Lastly, full efforts should be made to increase market diversification with the goal of
reducing dependency on conventional markets and anticipating the rise of new emerging
markets. The strategies could be implemented in trade negotiation and promotion as parts of
expansion toward targeted markets. Government should actively participate in trade
negotiations at bilateral and multilateral levels with the goal of minimizing trade barriers to
destination markets, including establishing free trade area agreements. Moreover, facilitating
export commodity exhibitions and promoting prospective buyers to trade are necessary to
acquire more access to potential markets.
PRATOMO Mochammad Hadi (MET06080)
39
References
Abdurohman & Zulfadin, R. (2002). Performance of Indonesia‘s key non-oil export during the
crisis: value vs quantity movement. Kajian Ekonomi dan Keuangan. 6(4), 91–103.
Ahmad, J. & Harnhirun, S. (1995). Unit roots and cointegration in estimating causality between
exports and economic growth: empirical evidence from the ASEAN countries. Economics
Letters. 49, 329–334.
Amrinto, L.E. (2006). A semiparametric assessment of export-led growth in the Philippines.
Unpublished master‘s thesis, Louisiana State University. Retrieved March 25, 2007, from
http://etd.lsu.edu/docs/available/etd-04072006-101336/
Asian Development Bank. (n.d). Key indicators 2006: measuring policy effectiveness in health
and education. Retrieved April 20, 2007, from
http://www.adb.org/Documents/Books/Key_Indicators/2006/default.asp
Bahmani-Oskooee, M., Mohtad, H. & Shabsigh, G. (1991). Exports, growth and causality in
LDCs: a re-examination. Journal of Development Economics, 36, 405–415.
Bresnan, J. (2005). Indonesia the great transition. Maryland: Rowman & Littlefield Publishers,
Inc.
Castro–Zuniga, H. (2004). Export-led growth in Honduras and the Central American region.
Unpublished master‘s thesis, Louisiana State University. Retrieved March, 25, 2007, from
http://etd.lsu.edu/docs/available/etd-12032004-070104.
Edwards, S. (1993). Openness, trade liberalization, and growth in developing countries. Journal
of Economic Literature, 31(3), 1358–1393.
Ekanayake, E.M. (1999). Exports and economic growth in Asian developing countries: co-
integration and error-correction models. Journal of Economic Development. 24(2), 43–56.
Enders, W. (2004). Applied Econometrics Time-series. New Jersey: Wiley International.
Engle, F.E & Granger. C.W.J. (1987). Co-integration and error correction: representation,
estimation, and testing. Econometrica, 55(2), 251–276.
Felipe, J. (2003). Is export-led growth passé? Implications for developing Asia. (ERD Working
Paper. No. 48). Asia Development Bank: Philippines.
Ghatak, S., Milner, C. and Utkulu, U. (1997). Exports, export composition and growth:
cointegration and causality evidence for Malaysia. Applied Economics, 29, 213–223.
Giles, J.A. & Williams, C.L. (2000). Export-led growth: a survey of the empirical literature and
some non-causality results part 1. Journal of International Trade & Economic
Development, 9(3), 261–337.
Goldman Sachs Investment Bank. (2003). Dreaming with BRICs: the path to 2050. Retrieved
April 15, 2007, from http://www2.goldmansachs.com/insight/research/reports/99.pdf
Gujarati, D.N. (2003). Basic Econometrics. New York: McGraw-Hill.
Hakim, L., Rachbini, D.J., Aminullah, E., and Pitono, D. (1996). Industrial and technology
policy for economic development in Indonesia. Centre for Analysis of Science and
Technology Department-PAPIPTEK LIPI, Jakarta.
Hill, H. (1999). The Indonesian Economy in crisis: causes, consequences and lessons. Institute
of Southeast Asian Studies, Singapore.
Hill, H. (2000). The Indonesian economy. Cambridge: Cambridge University Press.
Hill, H. and Shiraishi, T. (2007). Indonesia after the Asian crisis. Asian Economic Policy
Review. (2007)2, 123–141.
International Trade Center (ITC). Trade performance index: Indonesia. Retrieved April 20,
2007, from http://www.intracen.org/countries/toolpd03/idn_1.pdf
ITC. Specialization index of: Indonesia. Retrieved April 20, 2007, from
http://www.intracen.org/countries/toolpd03/idn_3.pdf
PRATOMO Mochammad Hadi (MET06080)
40
ITC. Trade performance index. Retrieved April 20, 2007, from
http://www.intracen.org/menus/countries.htm
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economics
Dynamics and Control, 12, 231–254.
Koop, G. (2000). Analysis of economic data. Chichester: John Wiley & Sons, Ltd.
Krugman, P. R. (1987). Is free trade passé? Economic Perspectives, 1(2), 131–144.
Krugman, P.R. and Obstfeld, M. (2006). International economics theory & policy. Boston:
Pearson-Addison Wesley.
Lütkepohl, H. & Krätzig, M. (2004). Applied time series econometrics. Cambridge: Cambridge
University Press.
Mah, J.S. (2005). Export expansion, economic growth and causality in China. Applied
Economic Letters. 12, 105–107.
Mamun, K.A., & Nath, H.K. (2005). Export-led growth in Bangladesh: a time series analysis.
Applied Economic Letters. 12, 361–364.
Matsumoto, Y. (2007). Financial fragility and instability in Indonesia. New York: Routledge
Contemporary Southeast Asia Studies.
Medina-Smith, E. J. (2001). Is the export-led growth hypothesis valid for developing countries?
A case study of Costa Rica. (Policy Issues in International Trade and Commodities Study
Series No. 7). University of Sussex, United Kingdom and Universidad de Carabobo,
Venezuela, United Nations, New York and Geneva.
Patterson, K.D., (2000). An introduction to applied econometrics: a time series approach.
(MacMillan, London).
Paul, S. & Chowdhury, K. (1995). Export-led growth hypothesis: some empirical testing.
Applied Economic Letters. 2, 177–179.
Phillips, P.C.B. & Perron, P. (1988). Testing for a unit root in time series regression.
Biometrika. 75, 335–346.
Prawiro, R. (2001). Indonesia’s struggle for economic development: pragmatism in action.
New York: Oxford University Press.
Rahman, M. and Mustafa, M. (1997). Dynamics of real exports and real economic growth in 13
selected Asian countries. Journal of Economic Development, 22(2), 81–95.
Ram, R. (1987). Exports and economic growth in developing countries: evidence from time
series and cross-section data. Economic Development and Cultural Change, 36, 51–72.
Richards, D.G. (2001). Exports as a determinant of long-run growth in Paraguay, 1966–96. The
Journal of Development Studies. 38(1), 128–146.
Shan, J. & Sun, F. (1998). On the export-led growth hypothesis for the little dragons: an
empirical investigation. Atlantic Economic Journal. 26(4), 353–371.
Shan, J & Tian G.G. (1998). Causality between exports and economic growth: the empirical
evidence from Shanghai. Australian Economic Paper. 37(2), 195–202.
Shirazi, N. S. & Abdulmanap, T.A. (2005). Export-led growth hypothesis: further econometric
evidence from South Asia. The Developing Economies. 63(4), 472–488.
Sinoha-Lopete, R. (2006). Export-led growth in Southern Africa. Unpublished master‘s thesis,
Louisiana State University. Retrieved March, 25, 2007 from
http://etd.lsu.edu/docs/available/etd-04072006-114156/
Todaro, M.P. & Smith, S.C. (2006). Economic Development. London: Pearson-Addison
Weasley.
Woo, W.T., Glassburner, B., & Nasution, A. (1994). Macroeconomic policies, crises, and long-
term growth in Indonesia, 1965–90. Washington: World Bank.
World Trade Organization. (1998). Trade policy review: Indonesia. Geneva: WTO.
WTO. (n.d). Project LINK world economic monitors. Retrieved April 20, 2007 from
http://www.chass.utoronto.ca/link/200010/WTOtrade1.pdf
PRATOMO Mochammad Hadi (MET06080)
41
WTO. (June 30, 2003). Trade policy review: Indonesia. Retrieved April 21, 2007, from
http://www.wto.org/english/tratop_e/tpr_e/tp216_e.htm
World Bank. (1993). The East Asian miracle: economic growth and public policy. Oxford:
Oxford University Press.
World Bank. (2006). World development indicators 2006. [CD-ROM]. World Bank.
Yang, M. (2002). Lag length and mean break in stationary VAR models. The Econometrics
Journal. 5, 374–386.
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Appendix A: Tables
Table 1. Augmented Dickey-Fuller and Phillips-Perron Unit Root Tests on
GDP, Exports, GFCF, and Labor for Indonesia (1980–2004)
Unit Root Test in Levels Unit Root Tests in First Difference
ADF PP ADF PP
Equation Series/Opt. Lag
(ADF)
Null Hypothesis
(Unit Root)
T-Stat Decision
I(d)
T-Stat Decision
I(d)
Series/Opt.
Lag (ADF)
T-Stat Decision
I(d)
T-Stat Decision
I(d)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Lg GDP
Constant No
Trend 0 γ =0 -1.49347 I(1) -1.49347 I(1) 0 -3.54087 I(1) -3.56325 I(1)
Constant
Trend 1 γ = a2 = 0 -1.32772 I(1) -1.12026 I(1) 0 -3.59527 I(1) -3.61675 I(1)
Lg EXP
Constant No
Trend 1 γ =0 -0.34479 I(1) -0.12513 I(1) 0 -6.02635 I(1) -6.2815 I(1)
Constant
Trend 0 γ = a2 = 0 -3.25208 I(0) -3.26828 I(0) 0 -5.86882 I(1) -6.0678 I(1)
Lg GFCF
Constant No
Trend 2 γ =0 -1.11714 I(1) -1.29919 I(1) 1 -3.73740 I(1) -3.11546 I(2)
Constant
Trend 1 γ = a2 = 0 -2.16213 I(1) -1.4235 I(1) 1 -3.69915 I(1) -2.94906 I(2)
Lg Lab
Constant No
Trend 0 γ =0 -0.96615 I(1) -0.95765 I(1) 0 -4.31058 I(1) -4.31121 I(1)
Constant
Trend 2 γ = a2 = 0 -2.0285 I(1) -1.79742 I(1) 0 -4.38845 I(1) -4.37700 I(1)
Note: ADF and PP test: Null hypotheses (levels and in first diff) at 10% level of significance: constant and no trend (γ = 0); constant and no trend
(-2.63); constant and a trend for γ = a2 = 0 (-3.24).
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Table 2. Correlation Matrix Analysis for Real GDP, Real Exports, Real GFCF and Labor
in Indonesia for period 1980–2004
Series Lg_GDP Lg_EXP Lg_GFCF Lg_LAB
Lg_GDP 1 0.957268 0.969382 0.975149
Lg_EXP 0.957268 1 0.932712 0.936205
Lg_GFCF 0.969382 0.932712 1 0.901210
Lg_LAB 0.975149 0.936205 0.901210 1 Note : - Series are stated in log term
Table 3. Cointegration Test on Bivariate Models for Indonesia (1980–2004)
Bivariate
Models
H0: r (Rank) Trace (λtrace) Max Eigenvalue
(λtmax)
RANK
Without
exogenous
variable
0 33.29147 30.21004 1
1 3.081437 3.081437
With exogenous
variables
0 51.55661 34.41005 2
1 17.14656 17.14656 Note: critical value at 5% for trace test is 15.49 at r = 0 and 3.84 at r = 1, critical value for max. eigenvalue test at 5% is 14.26 for r = 0 and 3.84 at r = 1.
Table 4. Results for the Residual Analysis for Indonesia (1980–2004)
A. Bivariate Model Without Exogenous Variable
Autocorrelation Normality
Up to lags Q-stats p-value Component Jarque-Bera df p-value
4 9.623084 0.0473 1 21.89594 2 0.0000
5 15.03114 0.0585 2 2.331583 2 0.3117
6 17.19013 0.1426 Joint 24.22753 4 0.0001
H0: No autocorrelation up to lag
specified. (at 5% critical value)
H0: Normally distributed residuals. (at 5% critical
value)
B. Bivariate Model With Exogenous Variables
Autocorrelation Normality
Up to lags Q-stats p-value Component Jarque-Bera df p-value
2 7.136716 0.1288 1 0.868330 2 0.6478
3 10.44619 0.2351 2 1.155400 2 0.5612
4 13.11070 0.3610 Joint 2.023731 4 0.7314
H0: No autocorrelation up to lag
specified. (at 5% critical value)
H0: Normally distributed residuals. (at 5% critical
value)
PRATOMO Mochammad Hadi (MET06080)
44
Table 5. Pairwise Granger Causality Test on Bivariate Full Rank Models with
Exogenous Variables for Indonesia (1980–2004)
Direction Lags F-Statistic p-value Conclusion
Exports GDP 1 0.93781 0.34386 NO
GDP Exports 1 50.9781 0.00000 YES
H0: No Granger causality between variables (at 5% critical value)
Table 6. Pairwise Granger Causality Test on Bivariate Models without
Exogenous Variables for Indonesia (1980–2004)
Direction Lags F-Statistic p-value Conclusion
Exports GDP
Short-run 3 0.05396 0.98280 NO
Long-run 3 0.04753 0.98572 NO
GDP Exports
Short-run 3 13.8202 0.00018 YES
Long-run 3 25.3959 0.00001 YES
H0: No Granger causality between variables (at 5% critical value)
Table 7. Long-term Development of World Exports
Periods A. Values (Growth Rate, %) B. Volumes (Growth Rate, %)
Total Agr. Mng Mnf. Total Agr Mng Mnf.
1950–1960 Average 7.7 3.6 8.8 10.7 7.7 4.9 8.1 8.7
1960–1970 Average 9.3 4.9 9.3 11.5 8.6 3.9 7.2 10.5
1970–1980 Average 20.4 16.6 27.2 19.1 5.3 3.5 1.7 7.1
1980–1990 Average 5.5 3.4 -1.4 8.3 3.9 1.5 1.0 5.6
1990–2000 Average 6.2 2.8 5.6 6.9 6.4 3.9 4.0 7.1
2000–2005 Average 10.1 9.0 9.0 9.2 4.7 3.6 2.7 5.1 Note: Agr. : Agricultural Mng. : Mining
Mnf. : Manufactures
Source: calculation based on WTO International Trade Statistics, 2006.
http://www.chass.utoronto.ca/link/200010/WTOtrade1.pdf
PRATOMO Mochammad Hadi (MET06080)
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Table 8. Indonesia’s Exports by groups of products, 1985–1997
(USD billions and percentage share)
Commodity 1985 1990 1991 1992 1993 1994 1995 1996 1997
Total (USD billions) 18.58 25.55 28.99 33.81 36.64 39.90 45.37 49.72 53.21
(Percentage Share)
Agriculture 15.9 16.2 16.5 14.9 15.0 17.6 18.0 17.0 16.1
- Food 10.0 11.2 11.3 10.2 10.8 12.8 11.4 11.2 11.4 - Agricultural raw
material 6.0 5.0 5.2 4.6 4.2 4.9 6.6 5.8 4.6
Mining 70.8 48.4 42.7 37.6 31.9 30.5 31.3 31.5 29.4
- Ores and other
minerals 1.5 2.6 2.9 3.0 2.7 3.1 4.4 4.4 3.5 - Non-ferrous
metals 2.7 1.8 1.3 1.2 0.8 1.0 1.6 1.3 1.2
- Fuels 66.6 44.0 38.5 33.3 28.4 26.4 25.4 25.8 24.6
Manufactures 13.0 35.5 40.8 47.5 53.1 51.8 50.6 51.4 42.3
- Iron & steel 0.2 0.9 1.0 0.8 0.8 0.8 0.8 0.7 0.6
- Chemicals 3.2 2.4 2.9 2.3 2.3 2.5 3.3 3.5 3.6
- Other semi-
manufactures 5.5 14.6 14.1 14.7 17.7 15.8 14.8 14.1 12.3
- Machinery and transport
equipment 0.5 1.4 2.3 4.3 6.0 7.6 8.4 10.0 8.7
- Textiles 1.3 4.9 6.2 8.5 7.2 6.3 6.0 5.7 4.2
- Clothing 1.8 6.5 8.0 9.5 9.7 8.2 7.6 7.4 5.6
- Other consumer
goods 0.5 4.6 6.4 7.5 9.4 10.6 9.6 10.0 7.4
Other 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.2
Source: Trade Policy Review Indonesia 1998 (WTO) pp. 131
Table 9. Indonesia’s Trade Performance Index, 1999–2003
Export Sector Share in National
Exports (%) Share in World
Exports (%)
Total Number of Exporting
Countries
Current Index Position
Wood Products 10% 2.79% 125 7
Clothing 7% 1.70% 117 10
Textiles 5% 1.62% 112 11
Minerals 29% 2.25% 151 14
Electronics components 5% 0.43% 99 17
IT & Consumer electronics 8% 0.68% 77 20
Leather Products 2% 1.49% 84 21
Fresh Food 7% 1.50% 173 25
Miscellaneous manufacturing 5% 0.51% 124 27
Processed Food 7% 1.40% 146 28
Chemicals 7% 0.48% 127 32
Basic Manufactures 5% 0.55% 130 32
Transport Equipment 1% 0.09% 97 42
Non-electronic machinery 2% 0.18% 107 66
Source: International Trade Center, http://www.intracen.org/countries/toolpd03/idn_1.pdf
PRATOMO Mochammad Hadi (MET06080)
46
Table 10. Indonesia’s Exports by destination, 1985–1997
(USD billions and percentage share)
Country 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 2001
Total
(USD billion) 18.5 25.5 28.9 33.8 36.6 39.9 45.3 49.7 53.2 48.9 56.3
(Percentage Share)
America 23.7 14.1 13.3 14.9 16.4 16.8 16.4 16.0 16.0 17.1 16.2
- United States 21.7 13.2 12.1 13.1 14.3 14.6 13.9 13.7 13.4 14.4 13.8
-Other America 2.0 0.9 1.2 1.9 2.1 2.2 2.5 2.3 2.6 2.7 2.4
Europe 7.5 12.9 13.9 15.3 15.8 16.0 16.1 16.7 16.3 17.3 15.0
Asia 66.7 70.3 69.1 66.1 64.2 63.7 63.7 63.3 63.2 60.2 63.0
- Japan 46.2 42.7 37.1 31.8 30.5 27.4 27.1 25.9 23.5 18.7 23.1
- China 0.5 3.3 4.1 4.1 3.4 3.3 3.8 4.1 4.2 3.8 3.9
- Middle East 1.2 2.7 3.2 3.5 3.5 2.9 3.0 3.0 3.4 3.4 3.3
- South Asia 0.7 0.8 0.8 0.9 1.1 1.5 1.5 1.8 2.2 2.6 2.9
- Other Asia 18.1 20.8 23.9 25.8 25.7 28.6 28.3 28.5 29.9 31.7 29.8
Oceania 1.2 1.9 2.3 2.5 2.4 2.0 2.4 2.7 3.1 3.5 3.7
Africa 0.9 0.8 1.4 1.2 1.3 1.4 1.4 1.3 1.5 1.9 2.1
Source: (1). Trade Policy Review Indonesia 1998 (WTO) pp. 138 (2). Trade Policy Review Indonesia 2003 (WTO) pp. 12
http://www.wto.org/english/tratop_e/tpr_e/tp216_e.htm
Table 11. Specialization Index of Indonesia (1999–2003)
Group of Commodities Rank Comparative Advantage
Wood Products 18 3.26
Textiles 20 1.89
IT & Consumer electronics 23 0.80
Leather Products 37 1.65
Electronic Components 47 0.50
Minerals 49 2.62
Clothing 50 1.98
Miscellaneous manufacturing 61 0.60
Chemicals 66 0.56
Processed Foods 68 1.62
Non-electronics machinery 74 0.21
Basic manufactures 77 0.64
Transport equipment 78 0.11
Fresh food 97 1.74
Note: The index measures the country's revealed comparative advantage in exports according to the
Balassa formula. The index compares the share of a given sector in national exports with the share of
this sector in world exports. Values above 1 indicate that the country is specialized in the sector under review. Rank 1 indicates that the country has the highest specialization index in the world for the sector
under review. Calculations based on COMTRADE of United Nations Statistics Division (UNSD).
Source: International Trade Center (ITC) http://www.intracen.org/countries/toolpd03/idn_3.pdf
PRATOMO Mochammad Hadi (MET06080)
47
Table 12. Indonesia’s Exports Performance by Ranking in Various Categories (2001–2005)
Sector description
Year Number of exporting countries
Ranking in net
exports
Ranking in share in world market
Ranking in product diver-
sification
Ranking in market diver-
sification
Ranking in Value
of exports
Ranking in export growth
Ranking in relative
unit value
Ranking in matching
with dynamics of
world demand
Ranking in compe-
titiveness effect
Ranking in adaptation
effect
Basic
manufactures
2001 131 33 34 32 23 34 66
2002 131 60 34 29 28 34 98 63 67 97 79
2003 131 34 35 31 23 35 89 63 17 80 78
2004 131 96 36 38 31 36 91 50 19 68 105
2005 131 102 34 46 33 34 70 54 37 90 38
Chemicals 2001 131 120 28 20 7 28 21
2002 131 114 28 19 7 28 64 18 60 57 89
2003 131 106 31 20 11 31 82 25 65 89 59
2004 131 116 31 21 17 31 86 23 69 74 106
2005 131 117 32 21 18 32 85 21 48 85 88
Clothing 2001 115 7 10 5 35 10 48
2002 115 7 12 5 45 12 97 51 48 95 43
2003 115 7 12 8 47 12 95 55 18 91 45
2004 115 8 14 4 57 14 90 51 52 83 27
2005 115 6 10 4 68 10 74 51 77 73 26
Electronic
components
2001 106 9 28 18 45 28 24
2002 106 6 28 20 42 28 50 34 5 9 103
2003 106 7 27 15 50 27 53 35 1 8 105
2004 106 7 27 13 45 27 61 35 1 3 106
2005 106 7 26 22 70 26 45 37 7 2 105
PRATOMO Mochammad Hadi (MET06080)
48
Indonesia‘s Exports Performance by Ranking in Various Categories (Contd.) Sector
description Year Number of
exporting countries
Ranking in net
exports
Ranking in share in world market
Ranking in product diver-
sification
Ranking in market diver-
sification
Ranking in Value
of exports
Ranking in export growth
Ranking in relative
unit value
Ranking in matching
with dynamics of
world demand
Ranking in compe-
titiveness effect
Ranking in adaptation
effect
Fresh food 2001 177 23 19 65 65 19 47
2002 177 18 18 62 66 18 42 49 96 72 55
2003 177 16 19 76 60 19 78 55 76 90 104
2004 177 14 19 84 58 19 65 50 43 90 90
2005 177 13 18 75 59 18 52 46 64 81 68
IT & Consumer
electronics
2001 81 10 22 2 39 22 28
2002 81 11 22 5 38 22 41 32 75 39 17
2003 81 12 23 4 38 23 62 34 80 62 34
2004 81 12 23 6 42 23 60 37 72 55 35
2005 81 12 23 7 47 23 58 35 67 51 29
Leather
products 2001 93 5 11 65 43 11 23
2002 93 6 16 69 43 16 86 28 8 83 70
2003 93 6 17 68 40 17 82 33 4 61 86
2004 93 6 16 65 35 16 82 35 1 27 90
2005 93 6 14 70 29 14 78 28 22 44 83
Minerals 2001 152 12 13 51 80 13 85
2002 152 12 13 33 82 13 106 82 40 48 102
2003 152 13 15 33 86 15 118 79 24 54 112
2004 152 19 28 19 38 28 147 72 41 111 105
2005 152 19 19 20 77 19 119 75 87 106 63
PRATOMO Mochammad Hadi (MET06080)
49
Indonesia‘s Exports Performance by Ranking in Various Categories (Contd.)
Sector description
Year Number of exporting countries
Ranking in net
exports
Ranking in share in world market
Ranking in product diver-
sification
Ranking in market diver-
sification
Ranking in Value
of exports
Ranking in export growth
Ranking in relative
unit value
Ranking in matching
with dynamics of
world demand
Ranking in compe-
titiveness effect
Ranking in adaptation
effect
Miscellaneous
manufacturing
2001 130 8 25 50 50 25 22
2002 130 8 25 57 51 25 102 30 61 102 82
2003 130 9 26 61 35 26 108 34 45 107 108
2004 130 13 27 50 40 27 108 31 21 103 48
2005 130 12 28 57 35 28 108 26 58 105 30
Non-electronic
machinery
2001 116 107 39 60 67 39 22
2002 116 104 39 76 75 39 22 21 19 24 60
2003 116 101 40 87 71 40 34 30 33 20 79
2004 116 104 41 69 64 41 43 33 7 19 95
2005 116 104 39 46 62 39 35 26 24 24 74
Processed food 2001 153 15 22 60 12 22 59
2002 153 12 18 79 20 18 18 46 138 42 25
2003 153 12 19 93 28 19 37 43 68 78 59
2004 153 9 18 88 29 18 26 22 132 42 36
2005 153 10 17 88 18 17 25 42 113 30 40
Textiles 2001 118 8 14 22 1 14 49
2002 118 9 15 21 1 15 88 50 32 89 67
2003 118 8 16 16 1 16 96 52 24 98 66
2004 118 8 16 11 1 16 98 47 16 96 60
2005 118 8 16 13 2 16 80 46 20 86 44
PRATOMO Mochammad Hadi (MET06080)
50
Indonesia‘s Exports Performance by Ranking in Various Categories (Contd.)
Sector description
Year Number of exporting countries
Ranking in net
exports
Ranking in share in world
market
Ranking in product diver-
sification
Ranking in market diver-
sification
Ranking in Value
of
exports
Ranking in export growth
Ranking in relative
unit value
Ranking in matching
with
dynamics of world
demand
Ranking in compe-
titiveness
effect
Ranking in adaptation
effect
Transport
equipment
2001 110 96 46 17 14 46 21
2002 110 92 43 10 9 43 36 22 16 21 88
2003 110 89 43 25 15 43 40 24 55 34 81
2004 110 84 45 11 19 45 46 28 42 37 75
2005 110 83 39 16 16 39 24 32 26 29 63
Wood products
2001 120 4 7 43 22 7 32
2002 120 4 7 39 24 7 76 36 28 4 117
2003 120 4 10 40 24 10 99 41 18 7 119
2004 120 5 13 36 26 13 106 40 46 10 119
2005 120 7 13 45 16 13 105 40 105 108 28
Source: International Trade Center (ITC)
http://www.intracen.org/menus/countries.htm
PRATOMO Mochammad Hadi (MET06080)
51
Appendix B: Figures
Figure 1. Four Macroeconomic Indicators
(Real GDP, Real Exports, Real GFCF and Labor) of Indonesia (1980–2004)
Note: - Value in y-axis stated in USD 2000 constant term except for labor which stated in unit. - Value in x-axis represents period of data from 1980 to 2004
4.00E+10
6.00E+10
8.00E+10
1.00E+11
1.20E+11
1.40E+11
1.60E+11
1.80E+11
2.00E+11
80 82 84 86 88 90 92 94 96 98 00 02 04
GDP
2.0E+10
3.0E+10
4.0E+10
5.0E+10
6.0E+10
7.0E+10
8.0E+10
80 82 84 86 88 90 92 94 96 98 00 02 04
EXP
1.0E+10
2.0E+10
3.0E+10
4.0E+10
5.0E+10
6.0E+10
80 82 84 86 88 90 92 94 96 98 00 02 04
GFCF
5.00E+07
6.00E+07
7.00E+07
8.00E+07
9.00E+07
1.00E+08
1.10E+08
80 82 84 86 88 90 92 94 96 98 00 02 04
LAB
PRATOMO Mochammad Hadi (MET06080)
52
Figure 2. Average Exports Composition by Commodities from 1988 to 2005
3%
2%
2%
2%
31%
3%
5%
1%
9%
3% 12%3%
1%
1%
4%
10%
1%
1%
2%
3%
0%
Animal and animal products
Vegetable products
Animal or vegetable fats
Prepared foodstuffs
Mineral products
Chemical products
Plastics and rubber
Hides and skins
Wood and wood products
Wood pulp products
Textiles and textile articles
Footwear, headgear
Articles of stone, plaster, cement, asbestos
Pearls, precious or semi-precious stones,
metals
Base metals and articles thereof
Machinery, mechanical appliances and
electrical equipment
Transportation equipment
Instruments—measuring, musical
Arms and ammunition
Miscellaneous manufactured articles
Works of art
Source: http://www.adb.org/Documents/Books/Key_Indicators/2006/default.asp
Figure 3. Exports by Commodities from 1988 to 2005
-
10,000.00
20,000.00
30,000.00
40,000.00
50,000.00
60,000.00
70,000.00
80,000.00
90,000.00
19881990
19921994
19961998
20002002
2004
Year
Val
ue
(in
mil
lio
n U
S$)
Works of art
Miscellaneous manufactured articles
Arms and ammunition
Instruments—measuring, musical
Transportation equipment
Machinery, mechanical appliances and electrical
equipment
Base metals and articles thereof
Pearls, precious or semi-precious stones, metals
Articles of stone, plaster, cement, asbestos
Footwear, headgear
Textiles and textile articles
Wood pulp products
Wood and wood products
Hides and skins
Plastics and rubber
Chemical products
Mineral products
Prepared foodstuffs
Animal or vegetable fats
Vegetable products
Animal and animal products
Source: http://www.adb.org/Documents/Books/Key_Indicators/2006/default.asp