The determinants of economic growth a case study of six southeast asian countries

INTRODUCTION

Problem statement

Throughout history, humanity has undergone significant changes and development Since the 19th century, rapid advancements have led to increased income and improved living standards, greatly enhancing people's welfare By the second half of the 20th century, many countries had doubled their real income per capita, marking a notable generational shift Notably, the rise of East Asian economies, often referred to as the "Miracle," represents a pivotal moment that has captured the attention of policymakers and researchers alike.

(a) Why are some countries rich, and other poor? How some countries develop very quickly and stably with their citizens enjoying rapid increases in their

(b) average incomes, meanwhile others development are very slow or not at all? Commented [m1]: So ambitious to solve in this study; should delete distinguish fast-growing from slower-growing countries?

After millennia of sluggish economic growth, the world economy underwent a significant transformation, raising questions about why such a change didn't happen sooner and why it emerged in specific countries rather than others.

Developed and developing countries strive to balance rapid economic growth with sustainable development, a complex challenge that lacks a one-size-fits-all solution Economists recommend various strategies to stimulate growth in developing nations One approach is to implement open commodity markets while remaining insulated from international capital markets to enhance economic stability and independence Additionally, developing countries can pursue industrialization through tailored policies, such as indirect subsidies, to foster their unique growth trajectories.

Thirdly, they can combine these policies above depending on specific conditions of

Economic growth is influenced by various determinants, including capital accumulation, labor force expansion, technological innovation, and institutional quality Each nation exhibits unique characteristics that shape its growth trajectory Developing countries, in particular, must carefully evaluate the most effective strategies to enhance their economic performance and achieve sustainable growth By identifying and implementing tailored solutions, these nations can maximize their economic potential and improve living standards for their populations.

Since its reunification in 1975, Vietnam transitioned from a Soviet-style central planning economy to a market-based system with the implementation of the "Doi Moi" reforms in 1986 This pivotal change marked the beginning of a new era, leading to significant economic growth and lifting Vietnam out of the least developed nations' category Research by Ngoc (2008) highlights that capital accumulation and labor have been the primary drivers of this growth, while contributions from human capital and technological advancements remain limited This trend is also observed in other Southeast Asian countries To ensure sustainable growth in the future, it is recommended that Vietnam focus on improving labor productivity.

Sustainable development is crucial for the growth of Southeast Asian countries, particularly Vietnam While annual economic growth is often seen as a positive indicator, it does not always guarantee sustainability and can sometimes lead to economic bubbles or overheating This study aims to analyze the contributions of key determinants, such as capital and labor, to the economic growth of Vietnam and other Southeast Asian nations, including Indonesia, Malaysia, the Philippines, Singapore, and Thailand, from 1993 to 2009 The findings will provide insights into the development landscape of Vietnam and its regional counterparts, offering valuable suggestions for future growth strategies.

Doi Moi, initiated in 1986, marks Vietnam's transition from a centrally planned economy to a market-oriented system This policy shift provides valuable insights for future strategic recommendations, guiding Vietnam towards sustainable long-term development.

Research structure

The content of this study is divided into 6 chapters:

Chapter II – Research objectives and research questions

Chapter V – Data analysis and discussion

Chapter VI – Conclusion and policy recommendation

Chapter II determines the Research Objectives and Research Questions which this study will carry out in next parts.

Chapter III - Literature Review explores key theoretical frameworks related to economic development and growth, focusing on the essential determinants of economic growth, particularly capital and labor This chapter also examines relevant empirical studies that analyze the connections between factor accumulation, productivity growth, and overall economic growth.

Indispensably that is Part IV - the Research Methodology which, present the method and procedure applied in this study to answer the Research Question.

The fifth part is Data Analysis and Discussion It’s very important part, in which analysis result will be presented through descriptive statistic analysis and econometric analysis as well.

Continuously, chapter VI concerns conclusion, from these information to point out suggestions that will help the economic growth of Southeast Asian generally andVietnam specifically achieve high and stability.

RESEARCH OBJECTIVES & RESEARCH QUESTIONS

Research objectives

The world experienced the fastest growth in the last half of the 20th century with the

The "golden age" of economic growth from 1950 to 1973 saw a remarkable annual increase of 3 percent in per capita income, particularly in developing countries post-World War II This growth is crucial for meeting fundamental human needs such as food, clothing, housing, health, and education, while also expanding individual choices and opportunities for leisure and cultural pursuits This study aims to analyze the impact of key macroeconomic factors, including financial capital and labor, on the economic growth of Southeast Asian nations—specifically Vietnam, Indonesia, Malaysia, the Philippines, Singapore, and Thailand—during the period from 1993 to 2009 The findings will reveal trends and the current state of Vietnam's economic growth, providing valuable insights for Vietnam to learn from the development experiences of its regional counterparts and to identify strategies for achieving rapid and sustainable growth in the future.

Research questions

As the identified objective above, this study would like to discuss the following important questions as follows:

[1] To what extent have capital, labor, population growth rate, and government expenditure contributed to economic growth of Southeast Asian countries?

Vietnam's economic growth has been significantly influenced by key factors such as capital investment, labor force expansion, population growth, and government spending in various industries Compared to other Southeast Asian nations, these elements have played a crucial role in enhancing productivity and fostering a favorable business environment, ultimately driving Vietnam's economic development and competitiveness in the region.

[3] To recommend general policies for sustainable development in term of economy and society of six Southeast Asian countries.

Research scope

This study aims to provide an overview of the economic growth trends in Southeast Asian countries, focusing on key determinants of growth in select nations It seeks to offer insights and recommendations for fostering rapid and sustainable economic development in Vietnam and other developing countries within the region.

Research contribution

While numerous research papers examine the determinants of economic growth globally, there is a noticeable scarcity of studies focused on Vietnam and Southeast Asia Countries in this region, including Vietnam, Indonesia, and Malaysia, are striving to enhance their economies to align with developed nations both regionally and globally Identifying the key factors that drive economic growth in developing countries is essential, as this knowledge will empower decision-makers to create effective strategies for economic advancement.

This study aims to partially explore and identify the key factors influencing economic growth in several Southeast Asian nations, including Vietnam, without delving deeply into the specific origins of wealth.

LITERATURE REVIEW

Theoretical Framework

The foundation of any growth theory begins with an aggregate production function, which defines the relationship between total output and the inputs used in production The most basic form of this production function can be expressed as follows:

This is the aggregate production function: the relation between aggregate output and two inputs.

K = the sum of all the machines, plants, and office building in the economy.

F = aggregate output tell how much output is produced for given quantities of capital and labor.

However, there are some restrictions reasonably impose on this function as follows:

- Increase in capital lead to smaller and smaller increases in output as the level of the capital increases, that property is so-called decreasing return to capital

- The similar property holds for the other output, labor: increase in labor, given capital, lead to smaller and smaller increase in output as the level of labor increases

this is decreasing return to labor as well.

Constant return to scale implies that we can re-write equation above as follows: Y/L = F (K/L; L/L) = F (K/L; 1)

It says that output per capita depends on capital per capita

In the long run, the relationship between output and capital is crucial, as the quantity of capital directly influences the level of output produced Conversely, the level of output impacts savings and investments, which in turn affects the accumulation of capital These interdependent relationships shape the progression of both output and capital over time.

One of extension form of Cobb-Douglas production function was built as follows:

Where: Y is yield, K is capital, L is labor, R is natural resource used, X is technology, β2 = capital's share of output, β3 = labor's share of output, β4 used natural resource's share of output.

According to modern ideas, X is the rest that effect on output Y and generally referred to total factor productivity (TFP), X includes human capital, technological efficiency, etc…

To derive the equation, we take the logarithm of both sides, resulting in the expression: log(Yt) = log(β0) + β2 log(Kt) + β3 log(Lt) + β4 log(Rt) + log(Xt) This transformation allows us to represent the relationship between output (Yt) and its determinants, including capital (Kt), labor (Lt), and resources (Rt), in a linear form, facilitating analysis and interpretation.

This equation is logarithm linear regression model.

During the 1940s, two economists Roy Harrod and Evsey Dorman (Robert G.King

In 1994, Ross and his colleague independently developed an economic growth model that utilizes a fixed-coefficient, constant return to scale function This model operates under the assumption that capital and labor are employed in a fixed proportion, resulting in a consistent output level.

Where: k is capital output ratio (COR).

The Capital Output Ratio (COR) measures the relationship between the amount of capital invested and the total output generated by that capital In contrast, the Incremental Capital Output Ratio (ICOR) represents the additional capital needed to produce one extra unit of output, calculated as the inverse of the increase in output relative to investment This concept, as described by E Wayne Nafziger (2006), also considers the impact of added excise taxes, which may be transferred to consumers based on demand and supply elasticity Mathematically, if Y denotes income and K represents capital stock, the ICOR can be expressed as K/Y.

The Harrod-Domar model is a straightforward economic framework that requires minimal data and is user-friendly, making it particularly effective for short-term growth forecasts, especially in developing countries However, its reliance on the assumption of equilibrium, where both labor and physical capital are fully employed, limits its accuracy for long-term economic predictions Additionally, the model fails to account for technological advancements and productivity improvements that are crucial for sustained long-term growth.

The foundation of any growth theory begins with an aggregate production function, which outlines the relationship between total output and production inputs This is exemplified by the Solow model, which can be further expanded to include additional factors such as technological advancement and human capital.

The Solow growth model illustrates the interplay between capital stock, labor force growth, and technological advancements in an economy, highlighting their collective impact on a nation's overall production of goods and services (Mankiw, 2009).

The evolution of economic growth theories has seen a significant shift from the Harrod-Domar model, which emphasizes the relationship between saving and investment for fostering growth through net investment, to the Solow growth model developed by Robert Solow in 1956 Utilizing Cobb-Douglas production, the Solow model is recognized as a standard neoclassical framework within long-term growth theory Solow posits that economic growth and development are dynamic processes influenced by changes in output, expenditure, capital, and population over time As a dynamic general equilibrium model, it operates within both discrete and continuous time frameworks, highlighting the interconnections between capital, labor, and technological advancements in relation to output.

Robert Solow developed a framework to explain growth sources in both developed and developing economies, positing that capital and labor can substitute for each other in production His model assumes constant returns to scale, perfect competition, and that the marginal productivity of input factors is positive yet diminishing The growth of an economy's output is determined by the quantity of inputs like capital and labor, along with technological progress Despite its limitations, the Solow model's significant strength lies in introducing a new explanatory variable in the production function and providing a more precise analysis of the disparities in GDP per capita across nations.

3.2.4 CAPITAL FUNDAMENTALISM: The Standard Perpetual Inventory Method with Steady-State Estimates of Initial Capital

According to Robert G King et al (1994), computing the capital stock series involves two primary methods The first method assumes that each country maintains a steady-state with a constant capital-output ratio, which eliminates the need to estimate the initial capital stock but relies on the assumption of a fixed capital-output ratio The second method employs the standard perpetual inventory approach, requiring an initial capital stock value; this method's advantage is that it does not necessitate assumptions about the ratios being analyzed.

This method is based on the assumption that the capital-output ratio (COR) is constant Therefore, the steady-state COR of country j is computed by: i j k j δ

It is gross investment in year t, Yt is GDP in year t j δ is capital’s depreciation rate i j is country j's steady-state investment rate ϕ j is country j's steady-state growth rate

Set the steady-state growth rate of country j as follows: ϕ j = λγ j + (1 − λ)γ w

Where: γ j is country j’s growth rate γ w is the world growth rate, set γ w =0.04 λ is a parameter that governs the relative weight we place on the country’s own experience, set λ = 0.25 b)

Perpetual inventory method, setting initial capital to zero

This method uses the formula to estimate capital stock as follows:

The initial capital stock, denoted as Ko, is estimated using the perpetual inventory method, starting with Ko=0 and accumulating investments over time to calculate the Capital Output Ratio (COR) While this approach simplifies the accumulation of investment, it has significant drawbacks, including the inability to produce a useful time series of capital stocks The initial estimate's impact diminishes slowly, suggesting that a more accurate estimate of the initial capital stock for countries would likely be more beneficial than starting from zero.

Perpetual inventory method: steady-state estimates of initial capital

This approach seeks to improve the initial capital estimation for all countries by adapting a technique proposed by Harberger in 1978 We will determine the initial capital using the steady-state method, followed by the computation of a time-series for the initial capital stock The steps for calculating the capital stock series are outlined accordingly.

Firstly, compute the steady-state growth rate of country j by equation: γ j = λγ j

Empirical Review

And then, to calculate the initial capital stock for a country, we rewrite this formula as:

K initial = k j Y initial (Harrod-Domar Growth Model)

Finally, the perpetual inventory formula produces a capital stock time series for country j.

3.3.1 DEVELOPMENT AND GROWTH OF VIETNAM AND SOUTHEAST ASIA

Research on Asia's economic growth highlights key determinants such as physical capital, labor, human capital, and total factor productivity (TFP), particularly in developing nations The rapid economic advancements in East Asia have reshaped conventional economic policies, leading many global economists to recognize two significant trends: the widespread diffusion of technological progress worldwide, diminishing the traditional advantages of Western countries, and a shift in the global economic center towards the Asian nations of the western Pacific These insights underline that the remarkable growth of East Asia aligns closely with rapid input growth, dispelling any lingering mysteries about the region's economic success (Paul Krugman, 1994).

Rapid growth in Asia has been driven by technological catch-up, with capital accumulation enhanced through technology transfer, which is essential for economic growth Endogenous growth theory, supported by Mankiw (2009), highlights the significance of physical capital as a primary source of economic growth in developing countries, contributing around 60% on average, followed by human capital and labor However, Kim and Lau (1994) argue that technological progress alone does not drive economic growth, as it requires new investment to impact output, attributing its benefits to capital accumulation Thus, capital emerges as the most critical factor for economic growth in developing nations.

Southeast Asia consists of 11 diverse countries: Brunei, Cambodia, East Timor, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam Each nation has its unique historical background, political systems, cultural identities, and geographical advantages, which influence their development trajectories Most of these countries are classified as developing nations, striving to enhance their economic status on the global stage Consequently, they are actively seeking to learn from the development experiences of more advanced countries and are prioritizing economic policies aimed at achieving rapid and sustainable growth.

Vietnam, as a developing country, continues to experience growth driven primarily by physical capital, which contributed 85% to 90% of GDP growth from 1996 to 2005 (P.M Ngoc, 2006, p 215) Labor also plays a significant role, accounting for 10% to 15% of GDP during the same period However, technological progress has been notably absent from Vietnam's economic growth between 1975 and 2005, showing little to no significant contribution to overall development (P.M Ngoc, 2006).

There’re many researches and working papers with different applied models that research on economics growth for a sole country, cross-countries or cross-regions.

Research studies focused on individual countries have been conducted by various scholars, including Ab Wahab Muhamad (2004) in Malaysia, Achara Chandrachai et al (2004) in Thailand, Caesar B Cororaton (2004) in the Philippines, Hananto Sigit (2004) in Indonesia, and Le Thanh Nghiep et al (2000) in Vietnam.

Vietnam's economic growth has been analyzed through various studies, including Nombulelo Duma's research in 2007 focusing on Sri Lanka, and Phan Minh Ngoc's work in 2006 and 2007 examining the determinants and sources of Vietnam's economic growth Additionally, Shandre Mugan Thangavelu's 2004 study on Singapore and Tran Tho Dat's research in the same year on Vietnam, along with Yan Wang et al.'s 2001 analysis of China, contribute to a broader understanding of economic dynamics in the region.

Various studies have explored cross-country analyses, including research by Charles R Hulten et al (2007) involving 112 countries, Laurits R Christensen et al (1980) covering 9 countries, and N Gregory Mankiw et al (1992) examining 121 countries Additionally, Robert J Barro conducted significant studies in 1989 and 1994, focusing on 120 and 100 countries, respectively, alongside collaborative work with Jong Wha Lee.

(1993) for 116 countries, Robert J Barro (1996) for 100 countries, Robert J Barro

(2001) for 10 Asian countries, Robert W Fogel (2004) for Asia, Steven N Durlauf et al (1998) for 122 economies, Susan M Collins et al (1996) for 7 East Asian countries, Svetlana Ledyaeva et al (2008) for 74 Russian regions…

Various explanatory variables can be incorporated into panel data growth models to assess their contributions to economic growth, as evidenced by numerous empirical studies Traditional variables, including physical capital, labor, human capital, and total factor productivity (TFP), have been extensively researched, notably by Laurits R Christensen and colleagues.

In 1980, research was conducted on nine countries, while N Gregory Mankiw et al expanded this analysis to 121 countries in 1992 Additionally, Shandre Mugan Thangavelu (2004) and Robert J Barro incorporated new variables into their studies, including the Foreign Direct Investment (FDI) to GDP ratio, government expenditure on education, the share of foreign equity ownership, and the share of exports to GDP.

(1989) measured investment in physical, and population growth; Robert J Barro

In their research, Robert J Barro and colleagues conducted comprehensive analyses on various factors influencing economic growth In 1994, they measured key indicators such as the Initial Level of GDP, Human Capital, Educational Spending, Fertility Rate, and Government Consumption, excluding education and defense They also assessed the Investment Ratio, Terms of Trade, and Democracy Earlier, in 1993, Barro examined the impact of the Black-market premium, male and female secondary school enrollment, and life expectancy on economic performance Additionally, in 1996, he focused on the inflation rate as a critical variable In a later study in 2003, Barro and Rachel M McCleary explored how religious factors, including church attendance and beliefs, contribute to economic growth.

The empirical study by Robert J Barro (1996) on the determinants of economic growth across 100 countries from 1960 to 1990 supports the concept of conditional convergence, indicating that higher initial schooling, increased life expectancy, lower fertility rates, effective rule of law, reduced inflation, improved terms of trade, and lower government consumption positively influence growth rates, especially when starting from a lower level of real GDP per capita Additionally, while an increase in political rights can boost economic growth at low levels of democracy, further expansions at moderate levels may hinder growth Conversely, a higher standard of living significantly correlates with a country's likelihood of achieving democracy The study employs an extended version of the neoclassical growth model to analyze these relationships.

Log (GDP_per_capita) = C + a * Log(Initial_GDP) + b *

Male_Secondary_&_HighSchool + c*Log(Life_Expectency) + d*Log(GDP)*Male_Schooling + e * Log(Fertility_rate) + f *

Government_Consumption_to_GDP_Ratio + g * Rule_of_Law_Index + h * Term_of_trade_Change + Democracy_Index + i * Democraxy_Index_Squared + j * Inflation_rate + Region_Area_Dummy_variable

Variable government consumption negatively impacts economic growth, as it reflects government spending that fails to enhance productivity This suggests that increased nonproductive government expenditure can lead to a decrease in the growth rate, given a specific starting GDP value Consequently, a larger government may hinder economic growth.

Regarding to researches on economic growth of 6 researched countries in this paper includes Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam.

A study by Hananto Sigit (2004) highlights that Indonesia's economic growth is primarily fueled by capital accumulation, particularly foreign direct investment (FDI), where increased capital and labor lead to higher output, and emphasizes the significant role of education in enhancing economic growth In contrast, research by Caesar B Cororaton (2004) indicates that the Philippines is experiencing predominantly negative total factor productivity (TFP) growth Additionally, findings from Shandre Mugan Thangavelu shed light on Singapore's economic dynamics.

Research indicates that the quality of labor, particularly skilled workers, significantly enhances Total Factor Productivity (TFP) growth, with education being a crucial element for long-term economic development In Thailand, a study by Achara Chandrachai et al (2004) revealed that from 1977 to 1986, economic growth was primarily driven by the expansion of capital and labor However, between 1987 and 1999, capital became the main contributor to economic growth, while TFP growth played a minimal role Similarly, studies in Vietnam, such as those by P M Ngoc (2006), highlight the importance of labor quality and education in driving economic progress.

In their 2007 study, Le Thanh Nghiep et al utilized time series estimation for Cobb-Douglas production functions, analyzing annual data from 1975 to 2005 Their research aimed to assess the contributions of capital formation, labor, and technological progress to the growth of the Vietnamese economy, while also evaluating the effects of economic reforms during this period.

RESEARCH METHODOLOGY

Variables

4.2.1 Dependent variable: GDP per capita

In this study, GDP per capita is proxy for economic growth.

+ Yit symbols for GDP, is output or yield of country i at year t In this paper, annual real GDP at price 2005 is proxy for Y.

+ Lit symbols for Labor, is the number of labor of country i at year t as well.

+ Hence, Yit/Lit is output yield per capita of country i at year t.

When conducting international research, it is essential to utilize per capita data rather than total output figures, as the standard of living is better reflected by output per capita Additionally, when comparing countries with varying population sizes, total outputs should be adjusted to account for these differences This adjustment is precisely what output per capita accomplishes (Blanchard, 2009:204).

4.2.2 Independent variables a)Capital per capita

+ Kit symbols for physical capital stock of country i at year t.

+ Hence, Kit/Lit is Physical capital per capita of country i at year t.

Currently, capital stock data for Vietnam and other developing countries is not easily accessible from existing sources, necessitating the computation of annual capital stock for this study This paper employs two distinct methods for calculating capital The first method is based on the work of Robert G King et al (1994), utilizing the perpetual inventory method with steady-state estimates of initial capital The second, simpler approach follows the methodologies of Le Thanh Nghiep et al (2000) and Phan Minh Ngoc (2006), where a specific value for the capital-output ratio (COR) is assumed for each country to derive the capital series.

Measuring capital stock by perpetual inventory method through some steps as:

Step 1: calculating the initial capital, then compute initial capital stock time-series

Two methods of computing capital have difference together in just this step.

Method 1 : bases on Robert G King et al (1994), Calculating the initial capital by using the steady-state method, then compute initial capital stock time-series.

+ Firstly, compute the steady-state growth rate ϕ of nation as follows: ϕ J = λγ J

In the research period, the growth rate of country j, denoted as γ J, is influenced by the world growth rate γ w, which is set at 0.04 The parameter λ, set at 0.25, determines the importance of the country's own growth experience relative to global trends Thus, the equation (1 − λ) γ w reflects the weighted contribution of the world growth rate to the country's growth dynamics.

+ The next, assumption of COR is fixed, we compute the COR as follows: k = i δ + ϕ where: + i = I t

It is gross investment in year t and Yt is GDP in year t + δ is depreciation rate of capital stock

+ ϕ is the steady-state growth rate

+ Finally, calculate the initial capital stock value in a certain year by formula as:

Method 2 : follow Le Thanh Nghiep et al (2000) or Phan Minh Ngoc (2006)

The value of COR (k) of countries should in range as table of Nghiep (2002) below.

In light of the economic conditions observed in Vietnam in 1986, we can project the Cost of Risk (COR) for Vietnam and other Southeast Asian nations This article presents a forecast of the COR range for each country under study during the specified research period.

+ Then, calculate the initial capital stock value in a certain year by formula as:

Step 2: using the perpetual inventory formula to produces few capital stock time series and takes it for running regression and compare the results together This step is the same for two methods of computing capital:

Where: δ is depreciation rate of capital stock

Kt is the capital stock in year t

Kt+1 is the capital stock in year t+1 (net year)

It is the increase in capital stock in year t

I t+1 is the increase in capital stock in year t+1 (next year)

In summary, the data we have to use to calculate capital stock comprises:

I - increase in capital stock annually or gross fixed capital formation. δ - depreciation rate of capital stock, set value δ=6% (T.T Dat calculated this rate, 2004)

In this study, I will calculate various capital stock series to test the hypotheses, starting with the initial capital stock from 1993 and generating the entire series through forward calculation Additionally, I will determine the initial capital stock from 2009 and create the complete series using backward calculation Furthermore, government expenditure will be analyzed as part of the research.

GovExp_to_GDP symbols the ratio of government expenditure to GDP (%) This expenditure, and includes education as well as defense, and the expected effect of this variable is positive.

Government expenditure, which encompasses general government final consumption expenditure on social services like education and health, plays a crucial role in a nation's economic growth Empirical studies, such as Robert J Barro's 1996 working paper "Determinant of Economic Growth: A Cross-Country Empirical Study," highlight the impact of government spending on economic performance Barro's analysis, excluding education and defense spending, indicates a significantly negative effect of government consumption on GDP growth This suggests that higher levels of nonproductive government expenditure and the corresponding taxation can hinder economic growth, implying that a larger government may adversely affect a country's economic development.

However, this paper uses total government expenditure including education as well as defense, thus expected sign will be positive. c) Population growth rate

Population growth, often referred to as the fertility rate, indicates the rate at which a country's population increases in a given year Typically, this growth rate is expected to have a negative sign, as higher population growth can adversely affect GDP per capita Additionally, a rising population requires more resources to be allocated to childrearing, diverting them away from the production of goods.

Data Description - Data collection – Data analysis

This study analyzes annual data from 1993 to 2009, covering 17 years, and focuses on key economic indicators such as GDP, labor, population, government expenditure, and physical capital for Southeast Asian countries, including Vietnam, Indonesia, Malaysia, the Philippines, Singapore, and Thailand While most of the data for these variables is readily available, capital data is currently lacking; therefore, I have computed the capital value series for each country using Gross Fixed Capital Formation (GFCF) at current prices.

2005 In general, the required secondary data for running regression model for this paper are GDP at price 2005, GFCF at price 2005, Labor, Population, Government Expenditure (including education and defence).

The used annual data of these countries are extracted from sources as follows:

The data on GDP at constant 2005 prices, Gross Fixed Capital Formation (GFCF) at constant 2005 prices, the growth rate of GDP at constant 2005 prices, and government expenditure at constant 2005 prices can be accessed from the United Nations website This information is essential for analyzing economic trends and understanding the financial health of nations over time For detailed statistics, visit the United Nations Statistical Division at http://unstats.un.org/unsd/snaama/resCountry.asp.

- Labor: total labor force data is extracted from CD World development indicators 2009, and Web site of ADB:

CD World development indicators 2009 http://www.adb.org/documents/books/key_indicators/2011/xls

This chapter outlines the conceptual framework grounded in theoretical foundations and empirical research It utilizes balanced panel data encompassing six cross-sectional units—Vietnam, Indonesia, Malaysia, the Philippines, Singapore, and Thailand—over a period of 17 years, from 1993 onwards.

The study analyzes annual GDP and labor data from 1993 to 2009 for a total of 102 observations across six countries Due to the unavailability of capital stock data, the paper computes value series of capital using two methodologies: one based on Robert G King (1997) and the other on Nghiep (2000) Both the traditional Solow model and an extended Solow model utilizing the Cobb-Douglas production function are employed through linear regression of panel data, with the Fixed Effects Model (FEM) identified as the preferred estimation method.

To check the robustness of the regression result, two models are applied in this study:

(1)ln(Yit/Lit) = α1 + α2D2i + α3D3i + α4D4i + α5D5i + α6D6i + β 2 ln(Kit/ Lit) + uit

With assumption is constant return to scale of labor and capital: β 1 + β 2 1

(2)ln(Yit/Lit) = α1 + α2D2i + α3D3i + α4D4i + α5D5i + α6D6i + β 2 ln(Kit/ Lit)+ β 3 ln(GovExp_to_GDPit) + β 4 (Pop_growth) it + uit

DATA ANALYSIS AND DISCUSSION

Descriptive Statistics Analysis

Figure 5.1 illustrates the GDP per capita trends from 1993 to 2009 for six Southeast Asian countries, indicating a consistent year-on-year increase in GDP per capita across all nations Notably, Indonesia's average GDP per capita reached $2,593 during this period.

In Southeast Asia, GDP per capita varies significantly among countries, with Singapore leading at $45,326, followed by Malaysia at $12,002, while Vietnam lags behind with only $1,019 The Philippines and Indonesia exhibit similar economic standings, each with a GDP per capita around $2,602, indicating a close economic relationship This disparity highlights the contrast between developed and developing nations in the region, with Singapore representing the highest economic achievement and Vietnam the lowest.

Figures 5.1: GDP per capita of 6 countries for the period of 1993-2009

Furthermore, we cannot deny the physical capital accumulation could cause an increase economic growth On average of years, Capita per capita is around $6,085 for Indonesia;

Singapore leads Southeast Asia with the highest GDP per capita at $110,469, indicating its status as the most developed nation in the region In contrast, Vietnam shows the lowest GDP per capita at $1,998, reflecting its position as a less developed country Other nations in the region include Malaysia at $28,338, Thailand at $17,158, and the Philippines at $5,088, highlighting the economic disparities among these countries.

Figures 5.2: Capital per capita GDP per capita for the period of 1993-2009

There are total 102 observations in the balanced panel, in which six specific countries for

Over a span of 17 years, the dataset reveals a balanced analysis with no missing values, showcasing an average annual capital per capita of $169,135 across six countries Notably, Singapore recorded the highest capital per capita at $110,469 in 2009, while Vietnam had the lowest at $864 in 1993 The findings indicate a direct correlation between capital per capita and GDP per capita, suggesting that an increase in capital is associated with a rise in income.

Following are tables of common descriptive statistics for variables used in applied models.

Table 5.1: Sample Observations - Descriptive Statistics - Sample: 1993 2009

SAMPLE OBSERVATIONS - Descriptive Statistics - Sample: 1993 2009

Desciption GDP/capita Capital/capita Labor POP_Growth GovExp/GDP

Table: 5.2: Correlation on the sample observations

Variables Log(GDP/Labor) Log(Capital/Labor) Log(POP_growth) GovExp/GDP

* POP_growth: Population growth rate *Capital is capital series No 1

* GovExp stands for government expenditure exclusive for education and defense

The correlation between GDP per capita and Capital per capita so high up to 0.98, and it’s a good sign as there’s high correlation between a dependent variable and an independent variable.

Econometric Analysis

This study aims to examine the factors influencing economic growth, focusing on fixed independent variables such as capital per capita, along with alternative variables including government expenditure per capita, the ratio of government expenditure to GDP, and the population growth rate.

5.2.1 WHETHER FEM OR REM IS MORE SUITABLE

Executing Hausman Tests to determine the best model should apply in this paper. Otherwise, we can compare the estimation result and see that FEM is more appropriate method below.

The results of different methods are in table and FEM seem to be a better. a) Model 1: Traditional neoclassical model

Table 5.3: Model 1 - a comparison of results with FEM

Ordinary Least Squares (OLS) regression assumes constant coefficients across time and individuals; however, in this analysis, OLS is deemed unsuitable due to the significantly high p-value of the common constant Consequently, Fixed Effects Model (FEM) or Random Effects Model (REM) are preferred alternatives to OLS for more reliable results.

Table 5.4: HAUSMAN TEST for MODEL 1

Based on the Chi-Square value of 3.82 and a p-value of 0.05, we reject the null hypothesis (Ho) that states the Random Effects Model (REM) is consistent and efficient This indicates that the REM is not consistent and efficient, leading to the selection of the Fixed Effects Model (FEM) for Model 2, which is the extended model.

Table 5.5: A comparison of results of Model-2 with FEM

In the result table, population growth has unexpected sign in OLS and REM estimation, result of FEM estimation is acceptable.

Table 5.6: HAUSMAN TEST of MODEL 2

As result above, with Chi-SQ of 4.72 and p-value of 0.19, thus reject Ho with

Ho: REM is consistent and efficient ie FEM is selected

Ln(Yit/Lit) = α1 + α2D2i + α3D3i + α4D4i + α5D5i + α6D6i + β 2 Ln(Kit/Lit)+ uit

In this analysis, If_VN represents Vietnam, while If_IN, If_PH, If_MA, and If_TL denote Indonesia, the Philippines, Malaysia, and Thailand, respectively The study operates under the assumption of constant returns to scale, indicating that proportional increases in input will lead to proportional increases in output across these countries.

Table 5.7: Estimated results for Model 1 with FEM

Statistically explanation : independent variable Log(Capital01/Labor) reaches statistically significant at level 1% (p-value is near zero, t-statistics is very high), thus this coefficient has significant meaning.

The model demonstrates an excellent goodness of fit, with R-squared and Adjusted R-squared values reaching as high as 0.99 This indicates that 99.9% of the variation in the dependent variable is effectively explained by the independent variables, highlighting a strong relationship between them.

The result of this regression is accepted and it can be written:

Log(GDP/L) = 2.89 - 0.49 If_VN - 0.25 If_IN + 0.35 if_MA - 0.16 if_PH + 0.87

If_SG - 0.34 If_TL + 0.6 * Log(K01/L) se = (0.0336) (0.0036) t = (86.1) (167.9) p= (0.000) (0.000)

And it also means that the coefficient of Labor is 0.4 (1 – 0.6) as assumption is constant return to scale.

This model examines the relationship between GDP per capita, capital per capita, and labor force It finds that a 1% increase in capital per capita results in an average 0.6% increase in GDP per capita, while a 1% increase in the labor force leads to a 0.4% increase in GDP per capita, holding other factors constant These findings underscore the significant impact of capital and labor on economic growth.

5.2.3 MODEL 2 - THE EXTENED NEOCLASSICAL MODEL

Ln(Yit/Lit) = α1 + α2D2i + α3D3i + α4D4i + α5D5i + α6D6i + β2 Ln(Kit/Lit)+ β 3 * (GovExp_to_GDP)it + β 4 * Ln(Pop_growth) it + uit

Statistically explanation : independent variables Log(Capital01/Labor), and

Log(Pop_Growth), and GovExp_GDP reach statistically significant at level 2% (p-value is less than 2%), thus this coefficients have significant meaning.

The model demonstrates an exceptional goodness of fit, with R-squared and Adjusted R-squared values reaching 0.999 This indicates that 99.9% of the variability in the dependent variable is accounted for by the independent variables in the model, effectively illustrating the strong relationship between them.

Table 5.8: Estimated results for Model 2 with FEM

Commented [m4R3]: Expend the estimated to get log(GDP/L)=LogGDP-LogL, Log (K/L) = LogK-LogL, we have the same result without the assumption.

Economically explanation: The regression result is accepted and it can be written:

Log(GDP/L) = - 0.79*If_VN - 0.34*If_IN + 0.47*If_MA - 0.27*If_PH + 1.17*If_SG -

+ 2.88 + 0.58*Log(K01/L) + 0.61*GovExp_to_GDP - 0.024*Log(Pop_Growth) se = (0.045) (0.0066) (0.150) (0.010) t = (64) (87.7) (4.16) (-2.41) p= (0.000) (0.000) (0.000) (0.018)

This model examines the relationship between GDP per capita and key factors such as capital per capita, the ratio of government expenditure to GDP, and population growth rate The results align with theoretical expectations and empirical studies Specifically, a 1% increase in physical capital per capita is associated with an average 0.58% rise in GDP per capita, while a unit increase in government expenditure relative to GDP results in a 0.61% increase in economic growth, assuming other factors remain constant Conversely, a 1% increase in the population growth rate correlates with a 0.024% decrease in GDP per capita, indicating a negative impact on economic growth, albeit a relatively minor one.

The limitations of data, and modeling techniques

In spite of trying all my best, there are some unavoidable limitations of the analysis related to limited statistic data or modeling techniques:

The study's limited time frame of just 13 years restricts the analysis of cross-country differences in elasticity concerning various independent variables This significant limitation hinders a deeper understanding of the economic structures and resource efficiency across countries, which is crucial for comprehensive panel analysis Consequently, the omission of this analysis represents a notable drawback of the research.

- Secondly, the Physical Capital figures of these countries are not available in common statistics database, thus I have to compute it with certain assumptions.

- Thirdly, the explaining variable Government Expenditure normally is computed with excluding education and defense, thus it has negative effect on economic growth (Barro,

1996) However, in this paper it includes education and defense consumption due to limited statistic data, thus the expected sign is positive.

This study focuses on measuring economic growth and development through selected economic and social indicators, while excluding environmental factors Notably, it omits several critical indicators, including human capital, technological progress, research and development innovation, political regime, inflation rate, democracy index, changes in terms of trade, and rule of law index.

This chapter addresses the study's objectives by summarizing the statistical analysis of GDP per capita and its independent variables—capital per capita, government expenditure as a percentage of GDP, and population growth—across six Southeast Asian countries from 1993 to 2009 The findings indicate a consistent increase in GDP per capita for most countries, with a significant relationship between GDP and traditional determinants in the applied models Various models, including OLS, FEM, and REM, were tested for overall significance and model selection, revealing that the Fixed Effects Model (FEM) with time effects is the most suitable The results suggest that capital and labor positively influence economic growth, while population growth negatively impacts it.

Moreover, the certain limitations are unavoidable and it can effect on this analysis result more or less.

CONCLUSION AND POLICY RECOMMENDATION

Main findings

This study aims to identify the determinants of economic growth across countries using a balanced panel dataset that includes Vietnam and several Southeast Asian nations, such as Indonesia, Malaysia, the Philippines, Singapore, and Thailand, from 1993 to 2009 Two equations, the neoclassical model and the extended-neoclassical model, are employed for analysis The findings indicate that capital, labor, and government expenditure positively contribute to economic growth, while population growth has a negative impact on growth.

The analysis of the traditional neoclassical model, which assumes constant returns to scale, reveals that in six Southeast Asian countries, capital per capita contributes 60% to economic growth, while labor accounts for 40% These findings highlight the significant impact of these factors on the region's economic development.

The analysis of the extended neoclassical model reveals that both capital per capita and government expenditure as a percentage of GDP have a significant positive impact on economic growth, while population growth negatively affects it Specifically, a 1% increase in physical capital per capita is associated with an average 0.58% rise in GDP per capita, and a unit increase in government expenditure relative to GDP results in an average 0.61% boost in economic growth Conversely, a 1% increase in population growth corresponds to a 0.024% decline in economic growth.

Empirical studies in developing regions, including Vietnam, indicate that the accumulation of physical capital is the primary driver of economic growth Additionally, labor and government expenditure play significant roles in this process Conversely, population growth tends to exert a negative impact on overall economic growth.

Economic Growth (GDP per capita) Government expenditure per GDP (+)

Figure 6.1: An overview of determinants of economic growth of 6 countries

Managerial Implications and Policies

Economic growth is defined as a stable increase in output per capita, reflecting improvements in productivity This process involves changes in the quantity and quality of production, expenditure structures, and encompasses transformations in politics, culture, society, institutions, and the environment For sustainable development, particularly in Southeast Asia, it is crucial that economic growth is driven by productivity improvements rather than mere capital accumulation from foreign investments Growth should integrate both quantity and quality, focusing on Total Factor Productivity (TFP) through effective investments in research and development and advanced human resources Additionally, economic growth must be linked to the development of democratic institutions and enhancements in social welfare, while also prioritizing environmental protection.

Vietnam, as a developing country, is currently prioritizing capital accumulation to drive economic growth, a common strategy among nations at this stage However, for long-term sustainable growth, it is crucial for Vietnam to pivot towards enhancing productivity and addressing other vital factors such as human development and environmental sustainability Achieving sustained growth necessitates continuous technological progress, which has significant long-term implications Key determinants of this technological advancement include investment in fundamental and applied research, the effectiveness of patent laws, and the emphasis on education and training.

The journey towards development and sustainability is inherently complex, as highlighted by Lindauer and Pritchett Therefore, a comprehensive guide to navigating this developmental path must encompass a multifaceted approach rather than a simplistic, linear direction (Colin White, 2009).

Limitations and future research

Besides the acceptable result as above, it still exist limitations that also are obstacles need to overcome in further research.

This study examines the average sample size, which includes only 102 observations from 6 countries over a span of 17 years While this sample size is deemed acceptable, it lacks high statistical significance Furthermore, the analysis does not address the cross-country differences in elasticity concerning various independent variables.

The accuracy of physical capital measurements poses a significant challenge, as the data for capital stock in many countries is often estimated based on specific assumptions This issue is particularly pronounced in developing nations, where reliable statistics on capital stock are frequently unavailable.

The data analysis is constrained by specific assumptions, including a uniform fixed depreciation rate of 6% applicable across all countries and time periods Additionally, the calculation of the Cost of Risk (COR) relies on a global growth rate of 4% over the research duration and assigns a relative weight of 25% to each country's historical experience, consistent for all nations at all times.

In this study, government expenditure is analyzed by including education and defense, which contrasts with the typical approach of excluding these sectors As a result, the expected sign of the relationship is positive, rather than the usual negative expectation.

This paper focuses exclusively on economic indicators, such as capital and government expenditure, alongside social indicators like labor, while omitting crucial environmental indicators Environmental factors significantly impact our health, quality of life, and production processes, highlighting their importance not only for humanity but also for the planet's sustainability Furthermore, Total Factor Productivity (TFP) is a vital contributor to economic growth and warrants serious measurement and analysis.

To achieve more significant results, future research should expand in scope by increasing the number of countries studied and extending the duration of the analysis Additionally, it is essential to examine cross-country variations in elasticity concerning different independent variables.

In addition to the variables analyzed in this paper, various other factors significantly influence economic growth To enhance the model's accuracy, it is essential to incorporate additional variables, including human capital, fertility rates, technological advancements, terms of trade, rule-of-law index, and investment levels, to better assess their contributions to economic development.

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REFERENCES FROM WEBSITES and OTHER

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APPENDICES APPENDIX A: Data of GDP, Capital, Population growth, Government expenditure

Table A.1: GDP per capita of sample observations

Table A.2: Population growth rate of sample observations

Table A.3: Government expenditure per GDP of sample observations

And here presents Physical Capital Series No 1 to No 5 of each country:

Table A.4: Capital series-01 per capita of sample observations

Table B.3: Description Statistics of the Philippines

APPENDIX B: Descriptive statistics of each country.

Table B.1: Description Statistics of Indonesia

Table B.2: Description Statistics of Malaysia

Table B.6: Description Statistics of Vietnam

Table B.4: Description Statistics of Singapore

Table B.5: Description Statistics of Thailand

APPENDIX C: Correlation Matrixes of each country.

Table C.1: Correlation Matrix of Malaysia

Table C.2: Correlation Matrix of the Malaysia

Table C.3: Correlation Matrix of the Philippines

Table C.4: Correlation Matrix of Singapore

Table C.5: Correlation Matrix of Thailand

Table C.6: Correlation Matrix of Vietnam

The result of the Correlation Matrixes of Indonesia, Malaysia, the Philippine, Singapore,Thailand, and Vietnam as tables, there’s no correlation between 2 explanation variables.

Figure D.1: Residual Graph of Model 1 – Traditional model – FEM, Capital Series No 1

Figure D.2: Residual Graph of Model 2 – Extended model – FEM, Capital Series No 1

Table E.1: OLS Regression Result of Model 1 – Traditional model,

Dependent Variable: LOG(GDP?/LABOR?)

Variable Coefficient Std Error t-Statistic Prob.

Adjusted R-squared 0.965005 S.D dependent var 1.244326 S.E of regression 0.232775 Akaike info criterion -0.058079 Sum squared resid 5.418401 Schwarz criterion -0.006609 Log likelihood 4.962048 Hannan-Quinn criter -0.037237

Table E.2: OLS Regression Result of Model 1 – Traditional model,

Table E.3: OLS Regression Result of Model 2 – Extended model, Capital Series No 1

Table E.4: OLS Regression Result of Model 2 – Extended model, Capital Series No.2

Table E.5: FEM Regression Result of Model 1 – Traditional model,

Method: Pooled EGLS (Cross-section SUR)

Linear estimation after one-step weighting matrix

Cross-section SUR (PCSE) standard errors & covariance (no d.f correction)

Cross-section fixed (dummy variables)

S.E of regression 1.034946 Sum squared resid 101.7557

Sum squared resid 0.516768 Durbin-Watson stat 0.260299

The analysis of GDP per labor across various countries reveals distinct relationships influenced by capital and labor ratios For Vietnam, the equation is expressed as LOG(GDP_VN/LABOR_VN) = -0.49 + 2.89 + 0.60*LOG(CAPITAL01_VN/LABOR_VN) In Thailand, it is LOG(GDP_TL/LABOR_TL) = -0.34 + 2.89 + 0.60*LOG(CAPITAL01_TL/LABOR_TL) India shows a similar pattern with LOG(GDP_IN/LABOR_IN) = -0.25 + 2.89 + 0.60*LOG(CAPITAL01_IN/LABOR_IN) The Philippines presents a slightly improved equation: LOG(GDP_PH/LABOR_PH) = -0.15 + 2.89 + 0.60*LOG(CAPITAL01_PH/LABOR_PH) In Malaysia, the relationship is more favorable as indicated by LOG(GDP_MA/LABOR_MA) = 0.35 + 2.89 + 0.60*LOG(CAPITAL01_MA/LABOR_MA) Lastly, Singapore exhibits the strongest correlation with LOG(GDP_SG/LABOR_SG) = 0.87 + 2.89 + 0.60*LOG(CAPITAL01_SG/LABOR_SG).

Table E.6: FEM Regression Result of Model 1 – Traditional model, Capital Series No.2

Table E.7: FEM Regression Result of Model 2 – Extended model,

Included observations: 17 - Cross-sections included: 6

====================• Log(gdp_vn/labor_vn) = -0.51 + 2.88 + 0.58*Log(capital01_vn/labor_vn) - 0.025*Log(pop_growth_vn) + 0.61*govexp_gdp_vn

• Log(gdp_tl/labor_tl) = -0.36 + 2.88 + 0.58*Log(capital01_tl/labor_tl) - 0.025*Log(pop_growth_tl) + 0.61*govexp_gdp_tl

• Log(gdp_in/labor_in) = -0.26 + 2.88 + 0.58*Log(capital01_in/labor_in) - 0.025*Log(pop_growth_in) + 0.61*govexp_gdp_in

• Log(gdp_ph/labor_ph) = -0.16 + 2.88 + 0.58*Log(capital01_ph/labor_ph) - 0.025*Log(pop_growth_ph) + 0.61*govexp_gdp_ph

• Log(gdp_ma/labor_ma) = 0.37 + 2.88 + 0.58*Log(capital01_ma/labor_ma) - 0.025*Log(pop_growth_ma) + 0.61*govexp_gdp_ma

• Log(gdp_sg/labor_sg) = 0.91 + 2.88 + 0.58*Log(capital01_sg/labor_sg) - 0.025*lLog(pop_growth_sg) + 0.61*govexp_gdp_sg

Table E.8: FEM Regression Result of Model 2 – Extended model,

Cross-section SUR (PCSE) standard errors & covariance (d.f corrected)

Table E.9: REM Regression Result of Model 1 – Traditional model, Capital Series No.1

Method: Pooled EGLS (Cross-section random effects)

Wansbeek and Kapteyn estimator of component variances

Adjusted R-squared 0.741840 S.D dependent var 0.145042 S.E of regression 0.073695 Sum squared resid 0.543095 F-statistic 291.2305 Durbin-Watson stat 0.246952 Prob(F-statistic) 0.000000

Table E.10: REM Regression Result of Model 1 – Traditional model, Capital Series No.2

Swamy and Arora estimator of component variances

Cross-section SUR (PCSE) standard errors & covariance (no d.f.

Table E.11: REM Regression Result of Model 2 – Extended model, Capital Series No.1

Table E.12: REM Regression Result of Model 2 – Extended model, Capital Series No.2

APPENDIX F: Main Empirical Studies Summary

Ord Table F.1: Main Empirical Studies Summary Type data & Dependent Independent

No Authors Objectives Countries Period model variable variable Result

TFP growth and its sources in the Malaysian economy from

1981 to 2001; identify the factors affecting TFP growth at the national level

Malaysia 1981-2001 Growth accounting framework with the production function.

- The growth rate of GDP was 6.6% from 1991 to 2001

- TFP growth was 1.5% per annum Mainly due to demand intensity which contributed 35.5%, followed by education and training (34.3%), capital structure (15.1%), technical progress (13.9%), and economic restructuring (1.2%).

Calculate the TFP from the energy crisis (1977-81), beginning of the expansion period (1982-86), boom period (1987-91), declining period (1992-96), and recession period after the financial crisis (1997-99)

- Quantify TFP growth and its contribution to overall economic

- growth of labor in the non-agriculture sector

- ratio of FDI to gross fixed capital formation

-The economic growth increased continuously since 1977 except during financial crisis in 1997- 1999.

-1977-1986: main source of economic growth was the expansion of capital and labor

-1987-1999: main contribution to the economic growth came from capital

-TFP growth played a very insignificant role in the contribution to growth.

-The contribution of TFP growth to economic growth consistently improved from -4.26% in the mid- p-255-279 possible factors that might have caused changes in TFP manufacturing growth.

4 Laurits R - International 9 countries: 1947-1973 Production Real - Real Capital input - Our principal finding is that Christensen, comparison United function Production - Real Labor input differences in growth rates of real

Dianne Cummings, of postwar patterns States, output - total factor product in 1960-73 are associated and Dale W of aggregate Canada, productivity with differences in growth rates of

Jorgenson 1980 economic growth France, - Times real factor input Increases and

“Economic - Assess the role of Germany, decreases in growth rates of real

Growth, 1947-73; growth in real Italy, product are associated with

An International capital input and in Japan, increases and decreases in growth

Comparison” real labor input in accounting for Korea, the

Netherland rates of real factor input

- That very rapid growth of real aggregate economic growth s, and the

United product is associated with rapid growth of both real capital input

Kingdom and real labor input, and that slow growth of real product is associated with slow growth of both inputs

- Moderate growth of real product can be associated with rapid growth of real capital input, rapid growth of real labor input, or moderate rates of growth of both inputs

5 Le Thanh Nghiep, The effect of Doi Vietnam 1986-1998 Production Economic - Capital stock - Doi Moi appeared to have a Huu Quy (2000)

“Impact Of Moi on Vietnam's

GDP. function growth -technical progress

- “Doi moi” significant positive effect on productivity, which by 1998

DoiMoi” accounted for a 42% increase in

- Financial crisis exerted a strong negative impact on the economic growth about 3.6% in 1998.

- High GDP growth in 1990s could be explained mostly by increase in

- Examines whether the Solow growth model is

1960-1985 Production function with cross-country

- Solow model is consistent with the int’ evidences if one acknowledge the important of human as well as

N Weil (1992) “A consistent with data physical capital These three contribution to the international variables do explain most ofnt’s empirics of economic growth” variation in the standard of living variation.

- Examines - Solow model that includes implications of accumulation of human as well as

The Solow model effectively illustrates the disparities in living standards across countries, highlighting that the accumulation of physical capital significantly influences income per capita Notably, poorer nations tend to experience faster growth rates compared to wealthier countries, suggesting that the model underestimates the impact of capital stock accumulation on economic development.

7 Nombulelo Duma Assess Sri Lanka’s Sri Lanka 1980-2006 Growth Economic - Capital stock - Labor was the dominant factor

(2007) “Sri sources of growth accounting growth - Labor force contributing to growth in the 1980s;

Lanka's Sources of framework - Human capital later, TFP took over as the main

Achieving sustainable medium-term growth hinges on establishing a stable political and macroeconomic landscape, executing essential structural reforms to enhance productivity and investment efficiency, achieving fiscal consolidation, and fostering an environment conducive to private sector development.

8 Phan Minh Ngoc Measure the Vietnam 1975-2005 Production Economic - Physical capital - Capital is main factor contributes

Technological contribution of Capital, Labor and Technological function growth - Labor

- Technology progress to economic growth account for 85% for economic growth

Progress in Progress to in growth

- Measures the contribution of capital formation,

Vietnam 1975-2003 Production function Economic growth

- The most important source of economic growth is capital accumulation.

Economic Growth” labor, and progress - Technological progress was technological - Doi moi statistically absent in the growth progress to the economic growth

- The impact of Doimoi on economic growth

10 Rober J Barro, Figure out relation Extended Economic - Government Expected sign is negative relation

1988 between growth Endogenous growth Consumption between grow rate of GDP per

Spending in a rate GDP per capita

Services ( that do not enter capita and level of government consumption expenditure per GDP.

Endogenous consumption expenditure per into households' production

11 Robert J Barro, 120 1960-1985 Models of Economic - Per capita growth Public investment tends to be

“A cross - country countries endogenous for

Data across growth - Investment in physical positively correlated with growth and private investment, property study of growth, countries - Human capital rights affect growth, strong negative saving and government”

- Government interaction between population growth and investment in human expenditures capital

12 Robert J Barro, 100 1960-1990 Panel data Economic - Maintenance of - Democracy is not the key to

1994 countries growth the rule of law economic growth, it enhances

“Democracy and - Free markets, growth at low levels of political

Growth” small government freedom but depresses growth when consumption,

- Human capital a moderate level of freedom has already been attained Democracy

- Initial Level of GDP on growth is weak negative

- Political freedoms tend to erode

- Initial Level of Human Capital, over time if they are out of line with a country's standard of living.

- Government Consumption probability that political freedoms will grow

- If economic freedom can be

- Investment Ratio established in a poor country,

- Terms of Trade growth would be encouraged & the

- Democracy country would tend eventually to become more democratic on its own

13 Robert J Barro & Sources of growth 116 1965-1985 Panel data Economic - Ratio investment - A country growth faster if begin

Jong Wha Lee, countries growth per GDP with lower GDP per capita relative

1993 - Ratio Government to initial level of human capital.

“Losers and consumption to - Positive effective on growth:

Winners in GDP Investment per GDP, Life-

Economic Growth” - Black-market premium expectancy,

- Male secondary large government, Black-market premium, revolutions. school - Female education has negative

- Female secondary effect on fertility. school - Male attainment has positive

- Revolution effect on primary school enrollment.

- Female and male attainment have positive effect on life expectancy, negative on infant mortality, positive on enrollment at second and higher levels.

14 Robert J Barro Measure the 100 1960-1990 Panel dataset, Economic - Initial_GDP level - Growth rate will get higher with (1996)- contribution of countries Neoclassical growth - Male seocondary higher initial schooling, higher life

“Determinants of factor to Economic model & HighSchool expectancy, lower fertility, better

Economic Growth- Growth - Life Expectancy maintenance of rule of law, lower

- Government Consumption to inflation, terms of trade improvement, and lower government consumption, lowerGDP Ratio initial level of real GDP per capita

15 Robert J Barro, One objective is to 10 1960-2000 Panel analysis Economic - Investment Ratios - The Asian financial crisis was

2001 assess whether the economies: focuses on the growth for real investment associated with a sharp reduction of

Economic growth in East Asia, particularly in Indonesia and Malaysia, experienced significant fluctuations due to currency and banking crises Before these crises, the region saw substantial private and public economic growth However, the lasting effects of these financial disturbances have contemporaneously reduced real GDP values, highlighting the vulnerability of the economies in this area.

The financial crisis has significantly impacted the growth prospects of various Asian economies, including Korea, the Philippines, and Thailand Economic performance indicators, such as stock market trends and investment rates, reveal the challenges faced by these nations By analyzing local economic conditions and converting them into actionable insights, we can better understand the dimensions of economic growth in the region, particularly in relation to China.

Hong performance in east Asia currency values of stock-market Asia have rebounded in 1999-2000, but the permanence of this recovery two groups of

Japan, indexes to U.S dollars and then is uncertain crisis had a long-term adverse effect

Taiwan measure of the U.S. price level

16 Shandre Mugan The importance of Singapore 1971-1998 Baseline model TFP - FDI to GDP ratio - Labor quality in terms of skilled

Thangavelu (2004), productivity growth growth - Government workers improves TFP growth

Productivity will be an important expenditure on education - The key component of the long- term growth in the Singaporean

Growth Survey component of long- - share of foreign economy is the quality of education

Report-Singapore” term growth in the equity ownership of the labor force: skill and

Asian countries as - share of exports to

GDP education plays a crucial role in economic development, highlighting a negative correlation between capital accumulation and total factor productivity (TFP) growth This suggests that there must be a period of "gestation" or "learning-by-doing" within the economy before positive signs of TFP growth emerge, indicating the importance of foundational educational investments for sustainable higher value-added growth.

”Economic Growth in East Asia-

Assimilation” is a crucial first step in extracting appropriate lessons from East Asian growth experiences:

Asian growth has been driven by sustained high rates of physical and human capital accumulation over several decades, coupled with a commitment to sacrificing current consumption to invest in future development.

(2)Or has the key been the less costly approach of adopting existing technologies from more advanced economies, which may be associated with increased capital accumulation along the way?

Indonesia, Malaysia, Philippines ,Singapore, Thailand, Taiwan, Korea with production function

- Philippines: TFP have negativ effect on growth in most times e

18 Svetlana Ledyaeva and Mikael Linden,

1996-2005 Dynamic Panel data examined empirically

Economic growth - Natural logarithm of per capita domestic

- Initial conditions (conditional convergence), domestic investment and exports are the most important

“Determinants of both determinants of investment ones for stimulating economic

Economic Growth: panel and short-run - Natural logarithm growth in Russia

Empirical Evidence from Russian cross- sectional economic growth in of per capita export

- Natural logarithm - Natural resource not contributed significantly, although the Russian

Regions” data Russian of resource index economy is traditionally considered regions 1996- - Natural logarithm to rely heavily on natural resources 2004; modified

Barro Sala-I- of per capita FDI - Export-led growth seems to be important in Russia during

The role of Foreign Direct Investment (FDI) as a growth determinant in Russia appears to be minimal, according to Martin's transition empirical analysis While neoclassical production theories suggest that FDI positively influences economic growth, this effect is reversed in high-income regions, where it can lead to negative outcomes.

19 Tran Tho Dat focuses mainly on Vietnam 1986-2000 Growth TFP - Human capital - To achieve sustainable growth, the

(2004) “Total TFP and its accounting - Technology economy needs a second wave of

Growth Survey determinants framework based upon an reforms concentrating on greater improvement in efficiency rather

Report-Vietnam”, aggregate than on more inputs.

P322-350 production - Efficiency growth has greatly function contributed to GDP growth In addition, labor productivity suggest the sustainable nature of economic growth in Vietnam.

- (1) labor reallocation in economic restructuring can greatly improve overall productivity;

- (2) Combination of FDI and absorption capacity of the economy plays an important role in efficiency gains.

(2001) “Sources of China's economic growth,

Tiêu đề	The Determinants Of Economic Growth: A Case Study Of Six Southeast Asian Countries
Tác giả	Nguyễn Kiều Giang
Người hướng dẫn	Dr. Nguyễn Minh Đức
Trường học	University of Economics
Chuyên ngành	Development Economics
Thể loại	Thesis
Năm xuất bản	2012
Thành phố	Ho Chi Minh City

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