Chapter 1: Background of Research
Introduction
Trade openness has long been considered a catalyst for economic growth, and this hypothesis
is even more apparent in the region of dynamic trade flows and economic integration, as in
Southeast Asia. Reducing trade barriers allows countries to access world markets, attract
foreign investment, and promote technology diffusion. Trade openness has a critical role in
shaping economic development within Malaysia and its neighbouring countries, including
Brunei, Thailand, the Philippines, Indonesia, Vietnam, Singapore, and Cambodia. The
relationship between trade openness and economic growth is complex and may be moderated
by factors such as technology sensitivity, human capital, and institutional frameworks (Nam &
Ryu, 2024).
According to the endogenous growth theory, advanced by Romer (1990), the driving
force for sustainable economic growth is based on technology and knowledge spillovers. In
this regard, the openness of an economy to trade improves the ability of a nation to access more
advanced technologies, which consequently accelerates the growth of productivity. However,
the level of this responsiveness depends on how sensitive an economy is to technology.
Technology sensitivity is defined as the degree to which a country can absorb, adapt, and
implement imported technologies into its domestic production processes. Given Malaysia's
trade-dependent economy and increasing investments in high-tech sectors, there is a strong
case to investigate how technology sensitivity mediates the relationship between trade
openness and economic growth.
Recent studies provide empirical evidence to support the theoretical links between trade
openness, technology sensitivity, and growth. For example, Nguyen and Bui (2021) show
evidence of a non-linear relationship between trade openness and economic growth in the
ASEAN-6 countries, with an emphasis on the crucial role of human capital and technology
absorption capacity. Kamsin et al. (2020) also established a long-run positive relationship
between openness to trade and economic growth; however, the authors surmised that
institutional factors and human capital are actually enabling factors that will drive home the
full benefits of open trade policies.
Despite the apparent gains in trade openness, challenges seem insurmountable. For
instance, the technological threshold of some ASEAN economies is low, and these are therefore
not well placed to maximize benefits from liberalized trade. This highlights that policies aimed
at complementarities in human capital and infrastructure should be pursued to facilitate
technological acceptance. Malaysia, which enjoys a relatively strong digital economy and
export-oriented industries, has the potential to play a leading role within the region in
maximizing its growth potential through trade openness.
This study aims to analyse the relationship between trade openness and economic growth
across different economic sectors in ASEAN economies. The model incorporates sectoral
variations to assess how openness affects productivity and growth differently in key areas such
as manufacturing, agriculture, and services.
The specific objectives are:
1. To quantify the impact of trade openness on economic growth across major sectors in
ASEAN countries.
2. To examine how human capital and institutional quality influence the sectoral effects
of trade openness.
3. To assess the mediating role of technology sensitivity in the trade-growth relationship
at the sectoral level.
4. To provide policy recommendations tailored to sectoral needs for maximizing the
benefits of trade openness in ASEAN economies.
The importance of this research is that it connects theoretical and empirical findings and
provides a detailed understanding of the channels through which trade openness affects growth.
Given the focus on technology sensitivity, this study contributes to the broader discussion of
economic policy, with implications for tailored strategies that could enhance regional
competitiveness and sustainability.
This study is guided by the following research questions:
1. How does trade openness affect economic growth across different sectors in ASEAN
economies?
2. Do the effects of trade openness on growth vary by sector (e.g., manufacturing,
agriculture, services)?
3. How do human capital and savings rate influence the relationship between trade
openness and sectoral economic growth?
4. To what extent does technology sensitivity mediate the impact of trade openness on
economic performance?
5. What policy recommendations can be made to maximize sector-specific benefits of
trade openness in ASEAN countries?
6. Diagnosis
7. Robustness
Chapter 2: Literature Review
Trade openness, as mentioned in the economic literature, has been one of the major driving
forces underlying growth in Southeast Asia, among other regions. In this review, findings of
recent studies will be synthesized, exploring how trade openness interacts with technology
sensitivity to influence long-term economic growth, focusing on Malaysia and her neighbours.
2.1 Trade Openness and Economic Growth
Many studies confirm the positive impact of trade openness on economic growth. For
instance, Nam and Ryu (2024) found that GDP in ASEAN countries was positively influenced
by trade volumes. The study stressed that while lowering trade barriers may stimulate economic
activities, too much openness can introduce vulnerabilities if not managed effectively. Nguyen
and Bui (2021) have also found similar evidence for a nonlinear nexus between trade openness
and growth in the ASEAN-6 countries. Their research shows that trade openness supports
growth up to a threshold level, beyond which higher openness might have diminishing returns.
Sriyana and Afandi (2020) investigated the asymmetric effects of trade openness in
selected ASEAN countries, including Malaysia. The results show that while increasing trade
openness is usually associated with higher growth, its reduction is associated with negative
effects. This asymmetry underlines the importance of stability in trade policies.
Kamsin et al. (2020) zeroed in on Malaysia, using data from 1980 to 2018. Their
findings confirm that there is a positive long-run relationship between trade openness and
economic growth, underlining how FDI and exchange rate play an important role in enhancing
these benefits.
2.2 Sectoral Dynamics in Trade Openness and Economic Growth
Sectoral composition plays a pivotal role in how trade openness translates into economic
growth. Different sectors such as manufacturing, agriculture, and services respond uniquely to
external trade liberalization due to their varying levels of exposure to international markets,
productivity levels, and labour intensities. Understanding this heterogeneity is essential for
crafting effective trade policies in ASEAN economies.
Recent studies have emphasized that the manufacturing sector tends to benefit more
from trade openness due to its integration in global value chains and higher productivity gains
from technology and foreign direct investment (FDI). For example, Nguyen and Bui (2021)
noted that in the ASEAN-6 countries, manufacturing output growth was more responsive to
trade openness than other sectors, largely due to greater access to foreign markets and capitalintensive operations.
Conversely, agricultural sectors, while still influenced by trade openness, face
limitations such as price volatility and vulnerability to external shocks. Sriyana and Afandi
(2020) highlighted that trade liberalization in agriculture requires complementary policies such
as infrastructure development and subsidies to offset exposure to global price fluctuations.
The services sector has shown mixed results, with growth often being driven by
financial services, tourism, and ICT subsectors that are highly dependent on regulatory
frameworks and human capital development. According to Setiyanto and Fitrady (2024),
liberalizing trade in services within ASEAN has improved GDP contributions, particularly in
digital services, although gains vary widely across countries.
This sectoral perspective is critical for ASEAN economies, where structural differences are
stark. Countries like Singapore, with a service-based economy, respond differently to trade
liberalization compared to more agriculture-dependent economies such as Cambodia or Laos.
Hence, understanding trade openness through a sectoral lens enables more nuanced policy
recommendations that consider the diversity within and across ASEAN nations.
2.3 Regional Comparisons
Comparative studies emphasize large differences in the way in which trade openness
and technology are exploited by regional neighbours. For instance, Hao (2023) examined the
case of China's trade openness and industrial development and found that FDI and capital
accumulation are crucial factors that reinforce openness. Cambodia and the Philippines,
however, have been constrained by technological and human resource readiness, according to
Nguyen & Bui (2021).
Malaysia's geographical strategic position as a hub for trade offers unparalleled
opportunities toward the integration of advanced technologies into its economic functions.
However, Albahouth and Tahir, (2024) added that governance and regulatory quality will
influence how well trade policy is effectively implemented. Gleaned from these presentations,
important insights have thus been developed for region-specific strategies, which balance of
trade liberalization against investments in technology.
2.4 Gaps in the Literature
While there is robust evidence from the existing literature on gains accruing from trade
openness, there are quite a number of gaps regarding how trade and technology sensitivity
interact, as well as regional dynamics. Few studies have comprehensively investigated how
these variables interact over time or account for external shocks such as global economic crises.
In addition, limited attention has been paid to country-specific policy interventions that can
enhance technological readiness and human capital.
In fact, this research tries to address these gaps by constructing a mathematical model
that combines trade openness, technology sensitivity, and economic growth in the case of
Malaysia and its neighbours. By considering dynamic interactions and region-specific contexts,
the study provides actionable insights for policymakers.
2.5 Theoretical Framework
This study integrates two important theories to understand the interplay between trade openness,
technology sensitivity, and economic growth: Endogenous Growth Theory and HeckscherOhlin Trade Theory. These frameworks provide a robust foundation for exploring the
mediating role of technology sensitivity in driving economic outcomes within Malaysia and its
neighbouring countries.
2.5.1 Endogenous Growth Theory
The endogenous growth theory, inspired by Romer (1990), places great stress on technology,
innovation, and human capital as binding constraints for long-term economic growth.
Knowledge spillover and technological changes brought about by openness to trade increase
the TFP. In this research, the relationship between the independent variable (trade openness)
and dependent variable (economic growth) is based on endogenous growth theory, where
technology sensitivity plays the role of a mediator. This fact supports the view that countries
with higher abilities in technological absorption gain more from trade openness for their
enhanced productivity and growth.
2.5.2 Heckscher-Ohlin Trade Theory
The Heckscher-Ohlin model (2008) puts forth how trade enables countries to specialize in the
production of goods for which they enjoy comparative advantage, therefore increasing resource
allocation and economic efficiency. The theory justifies the inclusion of trade openness as a
significant determinant of economic growth in the model. It highlights how Malaysia's and its
neighbours’ trade strategies impact their ability to adopt and benefit from advanced
technologies, which is central to this study.
These theories combined provide a framework that is in line with the research objective of
modelling the relationship between trade openness, technology sensitivity, and growth. This
theoretical basis shall, therefore, underpin the hypotheses developed and the methodological
approach followed in this study to ensure coherence and academic rigor.
2.6 Conceptual Framework
The conceptual framework for the present study relies on how the key variables are interlinked:
trade openness being the independent variable, economic growth being the dependent variable,
and sectoral performance being the mediating variable. It is hypothesized that trade openness
influences economic growth both directly and indirectly through its differential effects across
economic sectors, such as manufacturing, agriculture, and services.
Guided by the Heckscher-Ohlin model and endogenous growth theory, this framework explains
how trade openness facilitates specialization, resource allocation, and knowledge flows
mechanisms that operate differently across sectors. By analysing these sector-specific
dynamics, the framework offers a more nuanced understanding of how trade openness
contributes to long-term growth in ASEAN economies.
The conceptual framework supports the research objective of assessing the sectoral-level
impact of trade openness on economic growth within ASEAN, highlighting both direct and
structural transmission channels.
2.7 Hypotheses
The hypotheses of this study are based on the conceptual framework and theoretical
underpinnings drawn from Romer's endogenous growth theory and the Heckscher-Ohlin model.
They aim to test the relationship between trade openness and economic growth across different
sectors in ASEAN economies. The hypotheses are as follows:
1. H1: Trade openness has a positive and significant effect on economic growth in ASEAN
economies.
2. H2: The impact of trade openness on economic growth varies across economic sectors
(example: manufacturing, agriculture, services) in ASEAN.
3. H3: Human capital positively moderates the relationship between trade openness and
sectoral economic growth in ASEAN.
4. H4: Savings rate positively moderates the relationship between trade openness and
sectoral economic growth in ASEAN.
Chapter 3: Data and Methodology
3.1 Introduction
This chapter presents the methodology used to examine the impact of trade openness on
economic growth across sectors in ASEAN economies. It includes the model specification, data
sources, variable descriptions, and the estimation techniques to be employed. The approach
follows a panel data structure and is inspired by both the endogenous growth model and
empirical econometric techniques used in prior research such as Samundengu (2016).
3.2 Econometric Framework and Model Specification
To capture the sectoral dynamics of trade openness on growth, the study employs a sectorspecific panel data regression model, built on a Cobb-Douglas-type growth function with
extensions for trade openness and interaction effects. The panel consists of ASEAN countries
over the period 1990–2023, disaggregated into key sectors such as manufacturing, agriculture,
and services (based on data availability).
3.2.1 Model Specification
The baseline fixed-effects model is specified as:
3.3 Estimation Techniques
This study will use the following techniques:
1. Descriptive Analysis
•
Mean, standard deviation, min/max, and trends for all variables.
2. Panel Unit Root Tests
•
To test for stationarity of variables:
o
Levin-Lin-Chu (LLC) test
o
Im-Pesaran-Shin (IPS) test
o
ADF-Fisher and PP-Fisher tests
3. Co-integration Test (if variables are non-stationary at level)
•
Pedroni or Kao panel co-integration test
4. Panel Regression Estimation
•
Fixed Effects Model (FEM): To account for heterogeneity across countries and sectors.
•
Random Effects Model (REM): For comparison via the Hausman test.
•
Robust standard errors will be used to address heteroskedasticity and autocorrelation.
5. Granger Causality Test
•
If using time series within a sector or country: To determine causality direction between
trade openness and growth.
3.3 Theoretical Model
This study uses a mathematical model based on the Cobb-Douglas production function and its
extensions, which capture trade-related technology diffusion and human capital growth. The
equations are as follows:
Production Function
•
•
•
•
•
Y(t): Economic output.
A(t): Total factor productivity (TFP), driven by trade openness and technology sensitivity.
K(t): Physical capital.
H(t): Human capital.
α: Output elasticity of capital.
Human Capital Growth
•
•
•
•
H˙(t): Growth rate of human capital.
γ: Growth rate from domestic efforts.
ϕ: Trade-induced knowledge spillovers.
L(t): Labor force.
3.4 Data Analysis
This study employs econometric techniques to investigate the relationship between trade
openness, economic growth, and the mediating sectoral performance in Asean. The data
analysis will follow these steps:
The study adopts a panel data analysis approach, given the cross-country and time-series nature
of the data. The following techniques will be employed:
Descriptive Statistics:
•
Summarize the key features of the data, such as means, standard deviations, and trends
for trade openness, economic growth, human capital, and savings rate.
Unit Root Tests:
•
To ensure stationarity, unit root tests such as the Augmented Dickey-Fuller (ADF) and
Phillips-Perron (PP) tests will be conducted.
Panel Regression Analysis:
The baseline regression model examines the direct relationships between trade openness,
human capital, savings rate, and economic growth:
Explanation of Variables:
1. Dependent Variable:
o
GDP Growth: Annual GDP growth rate, which represents the economic
growth of country i in year t. This is the primary outcome being analysed.
2. Independent Variables:
o
Trade Openness: Trade-to-GDP ratio for country i in year t. It reflects the
degree to which the economy is open to international trade.
o
Human Capital : Proxy for the workforce's education and skill levels,
measured through indicators like mean years of schooling or the Human
Development Index (HDI).
o
Savings Rate: Gross savings as a percentage of GDP for country i in year t. It
reflects the population's capacity to save and reinvest in the economy.
3. Error Term:
o
ϵit: Captures unobserved factors that might influence economic growth but are
not included in the model.
Sensitivity Analysis
•
Evaluate the robustness of results by varying key inputs such as:
o
Trade openness values between 0.3 and 0.9.
o
Savings rates across a range of 0.1 to 0.5.
o
Human capital proxies (e.g., mean years of schooling vs. HDI).
3.5 Summary
This chapter outlines the methodology, including data sources, variables, mathematical
modelling, and analysis techniques. Using the MATLAB Optimization Toolbox, the study
ensures robust results by addressing trade openness, technology sensitivity, and human
capital’s roles in economic growth. This structured approach provides actionable insights for
policymakers while advancing theoretical understanding.
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