INTRODUCTION
Problem Statement
Climate change is one of the most important issues facing the world today
Global climate change has led to observable environmental impacts such as rising temperatures, intensifying hurricanes, increased droughts and heat waves, loss of sea ice, and accelerating sea level rise The primary cause of climate change is the emission of greenhouse gases, including water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), HFCs, CFCs, PFCs, and SF6 Among these, carbon dioxide poses the greatest risk due to its accumulation in the atmosphere, which heightens the threat of severe ecological problems This is primarily attributed to two key reasons, with CO2 being the most prevalent heat-trapping gas contributing to global warming.
Carbon dioxide (CO2) exhibits the highest positive radiative forcing (RF), playing a significant role in climate change Despite having a lower heat-trapping ability per molecule compared to other greenhouse gases, CO2's abundance and rapid emission through human activities make it the primary driver of global warming Additionally, CO2 remains in the atmosphere for a much longer duration than most other greenhouse gases, amplifying its long-term impact on climate change.
While methane takes about 10 years to decay and nitrous oxide takes a century, CO2 takes approximately 50-200 years to leave from the atmosphere
Many international conferences have been convened to address the urgent issue of reducing greenhouse gas emissions, particularly focusing on carbon dioxide Notably, the Kyoto Protocol, adopted in Kyoto, Japan, aims to establish binding targets for countries to cut their greenhouse gas emissions and combat climate change globally.
11 th December 1997, is a commitment of countries around the world to limit the greenhouse gases emission within the allowable levels After several rounds of
Radiative Forcing (RF) refers to the difference between the incoming solar radiation absorbed by the Earth and the outgoing radiation emitted back into space This factor directly influences the Earth's surface temperature; a positive RF indicates warming, while a negative RF suggests cooling.
7 | P a g e discussion and amendment (e.g Marrakesh, Morocco, in 2001; Doha, Qatar, in
2012), this protocol officially became effective on 16 th February 2005
Figure 1.1: Carbon dioxide levels since 400,000 years ago
(Credit: Vostok ice core data/J.R Petit et al.; NOAA Mauna Loa CO2 record.)
Research highlights that, beyond practical efforts to reduce global CO2 emissions, understanding the determinants of environmental and air pollution remains crucial, with corruption emerging as a significant factor Corruption, defined by Transparency International (2000), involves public officials improperly enriching themselves or associates through misuse of power Existing studies indicate that corruption impacts the environment both directly—by weakening the enforcement of environmental laws, leading to increased pollution (Hafner, 1998; Lippe, 1999)—and indirectly, through income transmission channels that influence pollution levels.
Corruption has been shown to have detrimental effects on economic growth, as evidenced by studies such as Mauro (1995) and Hall and Jones (1999) Additionally, environmental pollution can hinder sustainable development and economic progress Addressing these issues is crucial for fostering a healthy, thriving economy and promoting environmental well-being.
According to EKC theories, higher income levels and increases at lower income levels can influence environmental outcomes Therefore, it is essential to analyze the combined total effect—comprising both direct and indirect impacts—to determine whether corruption has a positive or negative impact on the environment This comprehensive examination helps clarify the ambiguous relationship between corruption and environmental quality.
Asia, of which the population was approximately 4,299 million people in
2013 (about 60% of the whole world population, UN DESA Population Division,
Asia, the largest continent, faces significant environmental and governance challenges, being home to some of the most polluted and corrupt countries worldwide This study analyzes the relationship between corruption, measured by the Corruption Perception Index (CPI), and carbon dioxide emissions across 42 Asian countries Utilizing a three-stage least squares method, our model—originally developed by Welsch (2004) and later refined by Cole et al (2007)—provides insights into how corruption influences environmental degradation The findings contribute to understanding the corruption–carbon emission nexus and offer policy recommendations, particularly for developing nations like Vietnam striving to improve governance and reduce emissions.
Research Objectives
Corruption directly influences greenhouse gas emissions by undermining environmental regulations and allowing illegal practices such as illegal logging, coal burning, and unregulated industrial activities to persist At various income levels, corruption weakens governmental oversight, leading to increased CO2 emissions as environmental standards are bypassed or inadequately enforced This detrimental effect occurs independently of other economic factors, highlighting how corruption alone can significantly elevate greenhouse gases in the atmosphere.
This study examines the indirect impact of corruption on CO2 emissions through its effect on economic growth, measured by income per capita We analyze how corruption hampers economic development and in turn influences environmental outcomes Specifically, our research addresses the question: “How does corruption indirectly affect CO2 emissions via income per capita?” By exploring this relationship, we highlight the significance of governance and economic prosperity in shaping environmental sustainability.
To determine the total effect, both the direct and indirect effects are combined Understanding these effects is essential for comprehensive analysis in research studies Accurately calculating the total effect provides a clearer picture of the overall impact, which is vital for making informed decisions and drawing valid conclusions.
Thesis Structure
The thesis is organized into several chapters, beginning with Chapter 2, which reviews existing literature on the relationships between corruption and economic growth, growth and the environment, and corruption and the environment Chapter 3 details the analytical framework, data sources, estimation methods, and explains the variables used in the study In Chapter 4, the authors describe the dataset and present their empirical results Finally, Chapter 5 offers conclusions, policy recommendations, suggestions for future research, and discusses the limitations of the study.
LITERATURE REVIEW
The corruption – growth relationship review
The relationship between corruption and economic growth is complex, with two main perspectives emerging in the literature Some theories suggest that corruption can potentially benefit the economy by facilitating business transactions and reducing bureaucratic delays However, a broader consensus indicates that corruption generally hampers economic performance by discouraging investment, distorting resource allocation, and undermining institutions Understanding these contrasting views is essential for analyzing how corruption impacts overall economic development.
Supporters of the former view argue that corruption can help avoid burdensome bureaucratic regulations and streamline administrative processes, effectively “greasing the wheels of bureaucracy” (Leff, 1964) According to Lui (1985), corruption reduces costs related to waiting time, enabling public officials to act more efficiently and make quicker decisions.
Myrdal (1968) suggests that when corruption accelerates administrative processes, public officials are incentivized to introduce more rigidity and uphold inflexible governmental procedures to increase opportunities for bribery.
Corruption can lead gifted individuals to pursue income through illegal activities rather than productive work, hindering economic development It discourages both local and foreign entrepreneurs from investing, as foreign businesses often face the need to pay bribes during the establishment process and ongoing payments to public officials to remain operational Overall, corruption impairs the foundation and growth of businesses, negatively impacting economic progress and development.
11 | P a g e and then, harms economic growth Furthermore, Rose-Ackerman (1997) and Tanzi
Corruption increases transaction costs and hampers the development of a market economy by fostering uncertainty that undermines free market systems, reduces government revenues, and raises public spending, making it difficult for governments to correct market failures due to compromised contract enforcement and property rights (1998) Additionally, corruption leads to resource misallocation, especially when investment decisions using public funds or private project endorsements are driven by corrupt gains rather than social value (Jain, 2001) Moreover, corruption can widen income inequality and exacerbate poverty, as social programs intended to assist the poor are often exploited by the wealthy for personal gain, ultimately impeding overall economic development (Gupta, Davoodi, and Alonso-Terme, 2002).
Numerous empirical studies have demonstrated that corruption negatively impacts economic development Mauro (1995, 1997) developed a single-equation model to analyze the effect of corruption on economic growth, utilizing Ordinary Least Squares and Instrumental Variables methods for estimation The findings consistently indicate that corruption significantly hampers economic growth, highlighting its detrimental influence on a country's economic progress.
Corruption, particularly through bribery, can significantly hinder private investment and economic growth Fisman and Svensson (2000) analyzed the relationship between bribery rates and short-term growth of Ugandan firms from 1995 to 1997, using data from the Ugandan Industrial Enterprise Survey Their findings indicate that higher levels of bribery, as a measure of corruption, are negatively associated with firm growth after controlling for variables such as firm size, age, foreign ownership percentage, and import/export activity This evidence underscores the detrimental impact of corruption on business development and economic progress.
12 | P a g e variables The result shows that if the bribery rate increase 1 percent, the firm growth will decrease 3 percent
Several other empirical studies confirm this result that there is a significant and negative association between corruption and economic growth existing [Méon and Sekkat (2005), Tanzi and Davoodi (2000)]
The empirical link between corruption and economic growth remains inconclusive, as some studies, such as Brunetti, Kisunko, and Weder (1998), find that corruption's impact on growth is often insignificant or diminishes when additional growth determinants are included Several researchers demonstrate that once control variables like structural reforms, investment, human capital, openness, or political stability are added to regressions, the previously observed significant relationship between corruption and growth tends to disappear For example, Abed and Davoodi (2000) show that the significance of corruption is statistically nullified when structural reforms are included in the model, while Mauro (1995) found no significant corruption-growth link after controlling for investment Similar results are reported by Pellegrini (2011), Pellegrini and Gerlagh (2004), and Mo (2001), indicating that the association between corruption and economic performance weakens or vanishes when accounting for key economic variables.
Recent empirical studies indicate that a country's institutional framework plays a crucial role in shaping the impact of corruption on economic growth These studies reveal a non-linear relationship between corruption and growth, suggesting that variations in institutional quality can significantly influence how corruption affects economic development For example, Mendez and Sepulveda (2005) provide evidence that stronger institutional settings may mitigate the negative effects of corruption on a nation's growth potential.
Research indicates that the relationship between corruption and economic growth varies across countries with different political systems In nations with high levels of political freedom, corruption can have a positive effect on growth, whereas in countries with limited political freedom, its impact remains unclear Aidt, Dutta, and Sena (2008) found that corruption negatively influences economic growth in countries with strong political institutions but has no significant effect in those with weaker institutions Additionally, Méon and Weill (2010) emphasize the critical role of institutional quality in shaping how corruption affects economic development.
Research indicates that in countries with weaker institutional frameworks, corruption has a less harmful impact on the economy Heckelman and Powell (2010) further support this, showing that in nations with a low economic liberty index, corruption can even positively influence economic growth However, as the economic liberty index increases, this positive effect diminishes, highlighting the complex relationship between corruption and economic development.
The relationship between corruption and economic growth remains highly ambiguous, with research offering varied perspectives Some studies provide both theoretical and empirical evidence that corruption negatively impacts economic growth, while others find no statistically significant correlation Additionally, certain researchers argue that the strength and nature of this relationship are influenced by the type of political institutions in place, which can determine the intensity of corruption's effect on economic development.
The growth – environment relationship review
The relationship between corruption and economic growth is often explained through the Environmental Kuznets Curve (EKC) hypothesis, which suggests that environmental degradation initially worsens as income levels increase However, after reaching a certain threshold or turning point, environmental quality begins to improve alongside continued economic growth.
The Environmental Kuznets Curve (EKC) hypothesis suggests that at low income levels, economic growth often leads to increased environmental degradation However, as income per capita rises beyond a certain point, environmental quality begins to improve due to greater environmental awareness and the adoption of cleaner technologies This pattern indicates an inverted U-shape relationship between economic development and environmental impact, highlighting the importance of sustainable growth strategies.
Economic development progresses from subsistence-level agriculture, which minimizes environmental degradation, to intensified agriculture, resource exploitation, and industrialization, leading to higher resource exhaustion rates that exceed resource replenishment As countries advance economically, they focus on developing information and service industries using modern technology, while increasing demand for better living environments and implementing stricter environmental laws to mitigate negative impacts.
Environmental degradation initially increases with income per capita but gradually declines as wealth continues to grow, leading to the concept of the "Environmental Kuznets Curve." This inverted U-shaped relationship suggests that economic growth can eventually promote environmental recovery once a certain income threshold is reached, resulting in environmental improvement over time (Panayotou, 1993).
Many empirical evidences about the EKC hypothesis applying for the case of
CO2 release have been provided with various results In particular, while Azomahou et al., 2006; York et al., 2003; or Roca et al., 2001 find that the association between
CO2 emission and income per capita is just linear, some authors, namely Cole
Key studies by Galeotti et al (2006), Heil and Selden (2001), and Galeotti and Lanza provide critical insights into the topic, highlighting significant trends and findings These researchers emphasize the importance of understanding emerging patterns in the field, which can inform future research and policy decisions Additionally, recent theses and academic papers further contribute to the body of knowledge, underscoring the ongoing evolution of the subject Accessing the latest full-text theses and academic resources is essential for staying updated with current developments.
Research by Agras and Chapman (1999) indicates that the relationship follows an inverted U-shape, with key turning points ranging from $20,000 to $60,000 Conversely, studies by Martinez-Zarzoso and Bengochea-Morancho (2004) and Sengupta (1996) identify an N-shaped curve, highlighting the temporary nature of the disconnect in this relationship.
Empirical studies have investigated the relationship between income levels and CO₂ emissions using country-level data For instance, Roca et al (2001) analyzed data from Spain between 1973 and 1996 to test the Environmental Kuznets Curve (EKC) hypothesis with six atmospheric pollutants, including CO₂, finding a strong positive linear relationship with an elasticity greater than 1 Similarly, Lindmark (2002) applied the approach of De Bruyn et al (1998) to examine the EKC in Sweden from 1870 onward, exploring fluctuations in CO₂ emissions in relation to income growth.
The study investigates CO2 emissions by examining key explanatory variables such as economic growth, fuel prices, cement prices, and technological changes within a structural time series model that accounts for stochastic trends Findings indicate that CO2 emissions are influenced by economic growth, while also emphasizing the importance of considering time-specific technological and structural changes when analyzing the Environmental Kuznets Curve (EKC) patterns Similarly, Friedl and Getzner (2003) explored the relationship between CO2 emissions and GDP for Austria from 1960 to 1999, employing various functional forms to identify the best fit; their results reveal an N-shaped relationship between Austria’s CO2 emissions and GDP, with a notable structural break in the mid-1970s attributed to an oil price shock.
The corruption – environment relationship review
While there is extensive research on the income–pollution and corruption–income relationships, studies on the corruption–environment link are still emerging Most existing research primarily focuses on the foundations of environmental policies rather than direct pollution outcomes, as highlighted by Fredriksson et al (2004), Damania et al (2003), and Fredriksson and Svenson (2003).
Lopez (1994) demonstrates that the Environmental Kuznets Curve (EKC) relationship is influenced by two key factors: the elasticity between traditional production components and pollution, and the relative slope coefficient of utility in income, also known as the relative risk aversion coefficient Economic growth may lead to increased pollution levels when these elasticities are low and the relative risk aversion coefficient is minimal, highlighting the importance of these factors in environmental and economic dynamics.
Lopez and Mitra (2000) suggest that society's risk preferences, reflected by the relative risk aversion coefficient, can be inferred from government policies They conclude that corruption exacerbates pollution issues beyond socially optimal levels, especially when there is cooperation between government and firms Their study also confirms that the Environmental Kuznets Curve (EKC) relationship persists despite corruption, but the EKC's turning point shifts to higher income and pollution levels under corrupt conditions.
Fredriksson et al (2004) investigate the impact of corruption on environmental and energy policy standards by developing a simple model that highlights the relationship between corruption and policy stringency Their model assumes that governments prioritize bribes and social welfare influenced by both employee and investor lobby groups, who must offer bribes to public officials to secure permits for increased energy use or less stringent policies, thereby improving labor productivity and capital efficiency The study considers industry size and coordination costs, concluding that higher levels of corruption lead to a decrease in the strictness of energy policies.
Corruption shifts the government's focus from social welfare to bribes and influence-buying, undermining environmental policies Studies by Damania et al (2003) and Cole et al (2006) reveal that the impact of trade liberalization and foreign direct investment on environmental policies is significantly affected by the level of corruption, with higher corruption weakening policy stringency Fredrikson and Svensson (2003) show that political stability influences environmental policy strictness differently depending on corruption levels—reducing stringency when corruption is low but increasing it when corruption is high Overall, corruption diminishes the effectiveness of environmental regulations, as supported by various anecdotal evidence.
Research by Desai (1998) reveals that corruption is widespread among public officials in developing countries and significantly contributes to environmental pollution In India, entrepreneurs often believe that bribing officials with a fee lower than the cost of complying with environmental laws is a common practice Evidence from Indonesia and Thailand indicates that vested interests influence public officials to weaken environmental regulations, undermining environmental protection efforts.
Recent research indicates that corruption has a direct and positive impact on environmental pollution However, these studies do not explore the underlying transmission channels, such as income levels, through which corruption may indirectly influence environmental degradation Understanding these mechanisms is crucial for developing effective policy responses to mitigate pollution caused by corrupt practices.
Welsch (2004) was one of the first researchers to explore both the direct and indirect effects of corruption on pollution, analyzing six indicators of air and water pollution across 106 countries His findings indicate that corruption has a positive direct impact on emissions, suggesting that higher corruption levels lead to increased environmental pollution Additionally, the study reveals that the indirect effects of corruption on pollution vary depending on income levels, being either negative or positive Notably, the direct effect of corruption on pollution is generally stronger than the indirect effect These insights imply that efforts to reduce corruption could enhance both economic growth and environmental quality However, Welsch’s study has limitations, such as relying on data from only one year and not accounting for the endogeneity of corruption, which may affect the robustness of the results.
Cole et al (2007) expanded Welsch’s model by treating corruption as an endogenous variable, utilizing Western European influence—measured by distance from the equator and the proportion of English speakers—as instrumental variables Their panel data analysis of 94 countries from 1987 to 2000 reveals that corruption directly increases CO2 and SO2 emissions, while also indirectly affecting poison gas emissions through its negative relationship with income per capita The indirect effect of corruption on pollution is negative but tends to intensify as income rises.
This thesis develops a simultaneous equations model based on the frameworks established by Welsch (2004) and Cole et al (2007) Utilizing panel data from 42 Asian countries spanning 2001 to 2013, the study tests the relationship between the key variables, providing robust empirical evidence The analysis offers valuable insights into the dynamics influencing economic outcomes across Asian nations.
We do not consider Western European influence as a factor for corruption, emphasizing a neutral stance in our analysis The use of instrumental variables in our study is carefully justified to ensure accuracy, even with limited data Our research aims to provide a comprehensive understanding of corruption dynamics without bias from external influences.
Asian countries since there is no big discrepancies in geographical location among these countries and the Asian mostly do not speak English as their first language
A different method called three-stage least squares (3SLS) is applied to improve estimation accuracy, as discussed in the next chapter.
METHODOLOGY
Analytical Framework
Air pollution is influenced not only by income levels but also significantly affected by corruption, according to existing literature It can be modeled as a function of per capita income and corruption levels, expressed as e = f(y, c), where e represents emissions, y denotes income per capita, and c indicates the level of corruption.
The corruption – emission relationship demonstrated by the partial derivative
e/c is expected to have positive sign It is argued that corruption might affect pollution through the establishment and enforcement of environmental regulations
According to EKC literature, environmental quality deteriorates steadily as income increases up to a certain threshold known as the "turning point." Beyond this point, environmental degradation tends to decline with further income growth, making the relationship between income and environmental quality ambiguous This signifies that the impact of income on environmental conditions changes after the turning point, highlighting the complex dynamics described by the Environmental Kuznets Curve.
Corruption not only directly impacts emissions but also indirectly influences air pollution through economic prosperity, as it has been shown to negatively affect income per capita According to the conventional production function, which models output based on total factor productivity, physical capital, and human capital, corruption hampers social infrastructure development Hall and Jones (1999) demonstrated that higher levels of corruption negatively impact social infrastructure, leading to reduced productivity Consequently, the relationship between corruption and income can be summarized through a function illustrating how corruption undermines economic growth and environmental quality.
21 | P a g e y = g (c,k,h) (2) where c = corruption level, k = physical capital per person, h = human capital per person
The total effect of corruption on poison gas emission is the sum of direct effect and indirect effect These effects can be expressed as the below formula
In this formula, e/ c represents the direct effect and (e/ y)(y/ c) is the indirect effect of corruption on emission through income channel
In this conceptual framework we use “Environment” as a generalized concept for “carbon dioxide emission”.
Model specification and estimation method
This study constructs an econometric model comprising two key equations to assess the impact of corruption on air pollution The first equation models income as a function of corruption, physical capital, human capital, population growth, inflation, and trade The second equation examines how corruption and income per capita influence the emission of toxic gases By analyzing these relationships, the research aims to quantify the overall effect of corruption on air pollution levels.
Indirect Effect tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This study examines the impact of various economic factors, including the share of industry in GDP and the share of trade (import and export) in GDP, on economic growth The regression models (Equations 4 and 5) incorporate key variables such as capital per worker, human capital, population, inflation, and trade openness The analysis emphasizes the significance of these factors in explaining variations in GDP, highlighting the roles of industry share and trade activities in fostering economic development The results demonstrate that increased trade integration and industrial share positively influence economic growth, underscoring their importance in policy formulation for sustainable development.
6 CORR it + it (4) LnE it = i + κ t + 1 CORR it + 2 lnY it + 3 (lnY it ) 2 + 4 (lnY it ) 3 + 5 lnIND it
This article examines the relationships between carbon dioxide emissions per capita (E) and various economic and institutional factors, including corruption level (CORR), per capita income (Y), industrial share in GDP (IND), trade share in GDP (TRADE), capital stock per worker (KPW), human capital (HK), population growth (POP), and inflation rate (INF) In the analysis, subscripts i and t refer to country and year, respectively Some variables are expressed in natural logarithms, with detailed explanations provided in the descriptive statistics section to enhance accuracy and interpretability Using these variables, the study aims to understand the key drivers of environmental impact across different nations.
The three-stage least squares (3SLS) method, developed by Zellner and Theil (1962), is used to estimate systems of equations by treating all equations simultaneously, addressing issues that arise in ordinary least squares (OLS) estimation This method solves two major problems: the correlation between endogenous variables and error terms, which violates OLS assumptions, and the potential correlation among disturbances across equations, especially when some independent variables are also regressands of other equations The 3SLS process consists of three stages: first, endogenous variables are instrumented using predicted values from regressions on all exogenous variables; second, each equation is estimated using two-stage least squares (2SLS) with these instrumented values to obtain a consistent covariance matrix of residuals; finally, this covariance matrix and the instrumented values are used to perform a generalized least squares (GLS) estimation, providing system-wide parameter estimates (Greene, 2003).
23 | P a g e that 3SLS’s estimation is better than 2SLS’s unless the system of equations is misspecified
We initially perform a pooled regression using a restricted model with only a single overall constant term Subsequently, we enhance the model by sequentially incorporating sub-region and time-specific effects, followed by the addition of country and time fixed effects These steps are essential to control for unobserved heterogeneity and ensure more accurate estimations of the primary variables of interest.
Data and variables
This paper uses a data set of 42 Asian countries in 2001 – 2013 period
Table 3.1 shows 42 Asian countries in detail which can be generally divided into seven sub-regions based on their geographical position and coastal boundaries
Table 3.1: Name of sub-regions and countries in the sample
1 Central Asia Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan,
2 East Asia China, Japan, South Korea
4 South Asia Afghanistan, Bangladesh, Bhutan, India, Nepal,
5 Southeast Asia Cambodia, Indonesia, Laos, Malaysia, Philippines,
Singapore, Thailand, Timor-Leste, Vietnam
6 Southwest Asia Armenia, Azerbaijan, Cyprus, Georgia, Turkey
7 West Asia Bahrain, Iran, Jordan, Kuwait, Lebanon, Oman, Qatar,
Saudi Arabia, Syria, United Arab Emirates, Yemen
Our variables, which are incorporated into Equations (4) and (5), are described in detail below, providing clarity on their roles in the analysis.
The Corruption Perceptions Index (CPI), published annually by Transparency International, serves as a key measure of corruption worldwide However, quantifying corruption remains challenging due to its complex nature; activities considered corrupt in one country or era may be seen as normal in another, and such illicit activities are often deliberately concealed to evade legal repercussions This concealment makes it difficult to accurately assess and measure corruption levels across different contexts.
Researchers commonly use perception-based assessments to measure corruption, with the Corruption Perceptions Index (CPI) being a widely recognized tool The CPI is compiled from diverse data sources sourced from reputable institutions and standardized on a scale from 0 to 100, providing a consistent measure of perceived corruption levels across countries.
In this study, the Corruption Perceptions Index (CPI) is rescaled inversely to the original data, ensuring that higher CPI scores indicate a greater degree of perceived corruption Originally, a score of 0 signifies the highest level of corruption, while 100 represents the lowest By reversing the scale, we facilitate clearer interpretation of the results, with higher CPI values directly reflecting increased corruption levels This adjusted measurement allows for more straightforward analysis and comparison of corruption perceptions across different contexts.
Income is modeled as a function of corruption, which itself may depend on income, necessitating the treatment of corruption as an endogenous variable Cole et al (2007) addressed previous study limitations by utilizing Western Europe's influence as an instrument variable for corruption, employing geographic distance from the equator and the percentage of native English speakers as proxies However, this study differs by analyzing the corruption–environment relationship using data from Asia rather than a global dataset, rendering these instrument variables less appropriate due to lack of geographical and linguistic differences within the region To mitigate endogeneity concerns, the study employs the Three-Stage Least Squares (3SLS) method, obtaining instruments for corruption through predicted values derived from regressions of corruption on all other exogenous variables within the system.
Air pollution is primarily measured by carbon dioxide (CO2) emissions, a key greenhouse gas released through human activities such as burning fossil fuels—including natural gas, coal, and oil—as well as from solid waste, deforestation, and chemical processes like cement manufacturing As the most significant greenhouse gas produced by humans, CO2 plays a major role in climate change Data on carbon dioxide emissions are obtained from the Emissions Database for Global Atmospheric Research (EDGAR), a collaborative project between the Netherlands Environmental Assessment Agency and the European Commission JRC Joint Research Centre.
Figure 3.2: Major Greenhouse Gases from People's Activities
Source: Intergovernmental Panel on Climate Change, Fifth Assessment Report (2014)
Economic growth (Y) is as common measured by GDP per capita which is extracted from World Bank data source
The industry’s share in GDP (IND), sourced from World Bank data, is incorporated into equation (5) to examine its impact on poison gas emissions This variable helps assess whether a country's economic sector composition influences its emissions levels It is anticipated that countries with a higher proportion of industry in their GDP will exhibit increased carbon dioxide emissions.
Greenhouse gases such as carbon dioxide, methane, nitrous oxide, and fluorinated gases play a significant role in global warming and climate change Carbon dioxide is the most abundant greenhouse gas emitted through human activities like burning fossil fuels Methane, with a higher global warming potential, is released from agriculture, livestock, and landfills Fluorinated gases, although less common, have a high impact on climate change due to their strong heat-trapping abilities Nitrous oxide, produced by agricultural practices and industrial processes, also contributes to the greenhouse effect Understanding the sources and effects of these gases is crucial for developing effective strategies to reduce emissions and mitigate climate change.
Trade openness, measured by the share of trade in GDP (TRADE), is a key component included in both equations (4) and (5) Numerous studies have been conducted to examine the relationships between trade, economic growth, and environmental impact, highlighting the significance of trade openness in these dynamic interactions.
Empirical studies indicate that open economies tend to achieve a steady state of growth more quickly than closed economies (Edwards, 1992, 1995, 1998) The relationship between trade openness and economic growth suggests that liberal trade policies enhance growth prospects by fostering greater market integration and resource efficiency Openness to international trade can accelerate economic development by encouraging technology transfer, increasing competition, and expanding access to global markets Overall, evidence supports the idea that trade openness positively impacts long-term economic growth and stability.
Research by Krueger (1997), Sachs and Warner (1995), and Ben-David and Kimhi (2000) indicates that openness to trade can promote economic growth through mechanisms such as reallocating resources, leveraging absolute and comparative advantages, and enhancing opportunities for technological innovation However, some studies suggest that openness may adversely affect economic growth by harming infant industries or creating balance of payments constraints, highlighting the complex impact of trade policies on economic development.
Trade liberalization might affect environment through three main effects: scale effect, technique effect and composition effect (Grossman & Krueger, 1991;
Liberalization-induced market entry leads to an increase in the size of the economy, known as the scale effect, which can potentially cause environmental degradation if other factors remain constant Conversely, the technique effect involves technological advancements in manufacturing methods associated with trade liberalization, which can improve environmental standards As trade and economic growth raise income levels, public awareness and demand for environmental quality and policies tend to increase, making the technique effect potentially beneficial for the environment.
The composition effect suggests that, with increasing trade openness, countries tend to specialize in activities where they have a comparative advantage This specialization leads to changes in the industrial structure of an economy, promoting more efficient resource allocation and economic growth As nations focus on their strengths, their industries evolve, enhancing competitiveness in the global market This process ultimately contributes to a more dynamic and specialized industrial landscape driven by the benefits of trade liberalization.
27 | P a g e alter The actual impact of the composition effect on the environment then is contingent on the determinants of country’s comparative advantage
The data of TRADE is gathered from World Bank data source
Capital per worker (KPW) is determined by dividing the total capital stock (K) by the labor force, providing a key measure of capital intensity in an economy While labor force data is readily available from the World Bank, obtaining accurate capital stock figures presents a challenge due to limited data To address this, researchers often employ the method of calculating the capital stock series through established estimation techniques, ensuring reliable analysis of capital accumulation over time.
“perpetual inventory method” is applied The perpetual inventory method will follow the formula:
Kt = Kt-1 - Kt-1 + GFKt = (1- ) Kt-1 + GFKt
The capital stock at time t (Kt) is determined by the gross fixed capital formation (GFKt), which can be sourced from World Bank data, and the depreciation rate (δ), typically assumed to be a constant 5% This relationship reflects how gross investment adds to the capital stock while depreciation reduces it over time, providing a comprehensive view of capital accumulation dynamics.
To calculate initial capital stocks, Hall and Jones (1999) applied the formula as follows:
RESULT
Descriptive Statistic
CO2 emission (E) (kiloton per year)
Adjusted Corruption Perception Index (CORR)
GDP per capita (Y) (current US$)
The share of industry in GDP (IND)
The share of trade in GDP (TRADE)
518 0.065 0.065 -0.181 0.544 2.44 14.57 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Table 4.1 presents the descriptive statistic of the panel data including 42 countries in 2001-2013 period
Asian countries collectively emit approximately 6,194 kilotons of CO2 annually, highlighting significant disparities in emission levels across the region Timor-Leste had the lowest CO2 emissions at just 1.16 kilotons in 2001, while Qatar recorded the highest with 55,383 kilotons in 2004 These figures emphasize the wide range of carbon emissions among Asian nations, reflecting varying levels of industrialization and energy consumption.
Due to recent increased attention to corruption, CPI data is unavailable for some countries in previous years Our dataset includes 481 observations, with an average CPI score of 63.69 In 2001, Bangladesh exhibited the highest corruption levels, reflected by an adjusted CPI of 96, whereas Singapore had the lowest CPI score, recorded at 6, indicating significantly lower perceived corruption.
Average GDP per capita of Asian countries is 8666 The spread between minimum and maximum value is really high An Afghanistan person had only about
120 US dollars in 2001 while Qatari had an income up to 96077 US dollars in 2013
Qatar is also a country having the greatest GDP per capita in the world currently
Industry averagely accounts for about 35% of GDP in Asian countries In
2006, industry only constitutes 6.9% of Timor-Leste’s GDP which is the minimum value in our industrial rate data The maximum value is 74.5% which was the industrial rate of Qatar in 2005
The mean of trade share in GDP is about 74% This figure reached the maximum of 345% in Singapore in 2006 and the minimum of 18% in Japan in
Capital per worker data has the mean of 28870 and varies from the minimum of 128 in Tajikistan in 2001 to the maximum of 295659 in Japan in 2013
The adult literacy rate across Asia averages approximately 84%, highlighting significant regional disparities Afghanistan has the lowest literacy rate at around 32%, indicating substantial challenges in education access, while Azerbaijan boasts the highest rate at nearly 99.79%, reflecting strong educational development This variation underscores the importance of targeted efforts to improve literacy levels in less literate countries and promote equitable educational opportunities across the continent.
The Asian population has been steadily increasing, growing at an average rate of approximately 1.9% annually Sri Lanka experienced a significant decline in its population growth rate in 2001, decreasing by about 1.6%, marking the lowest point in recent population growth data Conversely, Qatar demonstrated remarkable population growth, reaching the highest rate of 17.6% in 2007, reflecting rapid demographic expansion in the region.
Inflation rate in Asia is about 6.5% per year on average The minimum is - 18% expressing the deflation in Bhutan in 2004, the maximum is 54% which was Turkey’s inflation rate in 2001
Table 4.1 indicates that some variables significantly deviate from a normal distribution, as evidenced by skewness and kurtosis far from zero and three, respectively To achieve smoother data that more closely aligns with a normal distribution, all variables were tested with natural logarithms; if the logarithmic transformation resulted in improved normality, the variables were expressed in logs, otherwise, the original data were retained However, due to the presence of negative values in inflation rates and population growth, logarithmic transformation was not possible for these variables, leading to the omission of many observations.
Table 4.2 expressess the skewness and kurtosis value before and after taking natural logarithms and choices of variables’ form
Table 4.2: Skewness and kurtosis value before and after taking natural logarithms
Skewness/Kurtosis (after taking natural logarithms)
Variables chosen to put in the model
HK -1.30 / 3.54 -1.74 / 5.36 HK tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Covariance matrix
Table 4.3 presents the covariance matrix between variables, highlighting several significant correlations Notably, there is a negative correlation (-0.43) between emission and corruption, indicating that higher corruption levels are associated with increased air pollution Additionally, corruption is strongly negatively correlated with the economy (-0.74), suggesting that higher corruption may harm economic performance Conversely, the correlation between corruption and emission is positive (0.73), implying that air pollution tends to increase alongside economic growth.
Industry share in GDP positively impacts both emissions and income per capita, with correlation coefficients of 0.51 and 0.33 respectively Countries with a higher proportion of industry in their GDP tend to achieve greater income levels but also experience increased carbon dioxide emissions This suggests that industrial sector dominance is associated with economic growth alongside higher environmental impact.
The analysis reveals that trade is positively correlated with emissions, income, and industrial activity, with correlation coefficients of 0.29, 0.21, and 0.31 respectively, indicating that countries with higher import and export rates tend to have greater income, larger industrial shares, and higher pollution levels Conversely, the negative correlation of -0.34 between trade and corruption suggests that higher corruption levels may hinder countries from expanding their trade activities, potentially limiting economic growth and industrial development.
There is a strong positive correlation between capital per worker and air pollution, income per capita, share of industry in GDP, and share of trade in GDP Capital per worker, primarily measured through gross fixed capital formation, closely aligns with income levels, reflected by a high correlation coefficient of 0.96 As capital per worker increases, air pollution levels tend to worsen, with a correlation coefficient of 0.62 Additionally, countries with larger industry and trade shares in GDP generally exhibit higher capital per worker, indicating that economic activity and industrial growth are linked to increased pollution levels.
The analysis reveals a moderate positive correlation between capital per worker and industry share, with a correlation coefficient of 0.25, and a weaker positive relationship between capital per worker and trade share, at 0.14 Conversely, corruption shows a strong negative correlation with capital per worker, valued at -0.68, indicating that higher levels of corruption significantly hinder capital accumulation per worker.
Human capital, as indicated by literacy rate, positively correlates with key economic indicators such as carbon dioxide emissions, income per capita, industrial share, trade share, and capital stock, highlighting the interconnectedness between education and economic activity Conversely, higher literacy rates are associated with lower levels of corruption, suggesting that improved human capital can contribute to better governance and reduced corruption levels This relationship underscores the importance of investing in education to drive sustainable economic growth and promote transparency.
High population growth is significantly negatively correlated with air quality, indicating that countries with rapid population increases tend to experience worse air pollution Conversely, these countries often enjoy better economic growth, higher industry and trade shares in GDP, and greater capital per worker, suggesting a link between rapid population growth and economic development Additionally, nations with high population growth rates are more likely to face challenges such as increased corruption and lower literacy rates, highlighting disparities in social and economic indicators associated with demographic trends.
The analysis reveals that inflation rate negatively correlates with air pollution (-0.12), GDP per capita (-0.25), share of trade in GDP (-0.14), capital per worker (-0.27), and human capital (-0.08), indicating that higher inflation is generally associated with lower levels of these economic and environmental indicators Conversely, a positive correlation exists between inflation and corruption, with a coefficient of 0.35, suggesting that increased inflation may be related to higher corruption levels These findings highlight the interconnected impact of inflation, economic growth, environmental quality, and governance factors, offering valuable insights for policy development and economic stability strategies.
Figure 4.1 illustrates the significant correlations between key variables: corruption, carbon dioxide emissions, and income per capita The analysis highlights how higher levels of corruption are associated with increased CO2 emissions and variations in income per capita Understanding these relationships is essential for developing effective policies to address environmental degradation and economic inequality.
Figure 4.1: A combination of three scatter plots show the correlations between our main variables, namely corruption – carbon dioxide – emission, corruption – income per capita and income per capita – carbon dioxide emission
0 5 10 lnE lnY Fitted values tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
LnE CORR LnY LnIND LnTRADE LnKPW HK POP INF LnY 2 LnY 3
CORR -0.43*** 1 LnY 0.73*** -0.74*** 1 LnIND 0.51*** -0.11** 0.33*** 1 LnTRADE 0.29*** -0.34*** 0.21*** 0.31*** 1 LnKPW 0.62*** -0.78*** 0.96*** 0.25*** 0.14*** 1
HK 0.71*** -0.24*** 0.49*** 0.32*** 0.35*** 0.36*** 1 POP 0.15*** -0.25*** 0.30*** 0.18*** 0.20*** 0.25*** -0.15*** 1 INF -0.12*** 0.35*** -0.25*** -0.02 -0.14*** -0.27*** -0.08* -0.05 1 LnY 2 0.71*** -0.75*** 0.99*** 0.31*** 0.21*** 0.95*** 0.47*** 0.33*** -0.26*** 1 LnY 3 0.70*** -0.76*** 0.98*** 0.29*** 0.21*** 0.94*** 0.44*** 0.36*** -0.26*** 0.99*** 1
This article discusses the importance of academic research and thesis writing, emphasizing the significance levels at 99%, 95%, and 90% to ensure the reliability of findings It highlights the process of completing a master's thesis, including the necessary steps to achieve successful graduation Additionally, the content points out the availability of resources, such as downloadable thesis templates and guidance, to support students in their academic journey For further assistance and access to full thesis examples, interested individuals can contact via email at (provide email address) Ensuring proper research methodology and adhering to academic standards are essential for producing high-quality theses that meet graduation requirements.
Regression result
Table 4.4 presents the results of a pooled regression analysis using the 3SLS method with a restricted model that includes only a single overall constant term The analysis, based on Equation 4, models GDP per capita as a function of corruption, capital stocks per worker, human capital, population growth, inflation, and the share of trade in GDP The findings indicate a positive but statistically insignificant relationship between corruption and GDP per capita.
Capital per worker significantly boosts income per capita, with a 1% increase in capital per worker leading to a 0.876% rise in GDP per capita, ceteris paribus Human capital, measured by literacy rates, positively impacts income, where a 1% increase in literacy rates results in a 1.2% income growth Additionally, a 1% increase in population growth can elevate GDP per capita by 5.85%, holding other factors constant While negative inflation-income and positive trade-income relationships are observed, these effects are statistically insignificant Higher corruption perception (a 1-point increase) directly increases CO2 emissions by 0.074%, and countries with larger shares of imports and exports tend to emit more carbon dioxide, where a 0.01% rise in trade shares causes CO2 emissions to increase by 0.738% The analysis reveals a consistent positive relationship between income per capita and emissions, indicating that within the studied GDP range, higher income levels are associated with increased gas emissions The pooled regression results confirm a direct effect of corruption on CO2 emissions, but the indirect impact via income remains ambiguous due to the lack of a significant relationship between corruption and income.
Table 4.4: Three-stage least squares regression (pooled regression)
Endogenous variables: LnY LnE CORR Exogenous variables: LnKPW HK POP INF LnTRADE LnIND LnY 2 LnY 3
To differentiate the effects attributable to country-specific features and temporal changes, both cross-sectional and time effects are incorporated into the equations Table 4.5 and Table 4.7 display the results after accounting for sub-regions and country effects, respectively This approach allows for a comprehensive understanding of regional and national influences on the analyzed variables, ensuring more accurate and nuanced insights.
Table 4.5: Three-stage least squares regression with fixed effects of sub-regions and time
2013 dummy -0.118 -2.067*** tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This analysis examines the impact of endogenous variables such as LnY, LnE, and CORR, alongside exogenous variables including LnKPW, HK, POP, INF, LnTRADE, LnIND, and lagged values of LnY for 2002, 2003, and 2004 The study spans multiple years from 2005 to 2013, incorporating regional variations across regions 2 through 7 to capture geographical heterogeneity Incorporating both economic indicators and regional identifiers enhances the understanding of factors influencing the outcomes over this period, aligning with best SEO practices for economic and regional analysis.
***, ** and * denote significance at 99%,95% and 90% respectively
Adding differential intercepts for sub-regions and time reveals more significant relationships, as shown in Table 4.5 Notably, the effect of corruption on income, previously insignificant in Table 4.4, becomes significant in Equation 4, with a 1-point increase in the corruption perception index resulting in a 0.007% rise in income All control variables in Equation 4 are significant; capital per worker, human capital, and population growth positively influence GDP per capita, while inflation exhibits an inverse relationship The dummy variables for sub-regions are significant, and only the dummy variable for 2009 is significant among the years In Equation 5, all variables are significant except for the share of industry in GDP and the dummy variables for 2002 and 2003, which are not significant.
Corruption has a significant positive relationship with CO2 emissions per capita, with a 1-point increase in the corruption perception index leading to a 0.04% rise in emissions Additionally, there is a notable association between per capita income and CO2 emissions, where the marginal effect (δE/δY) remains positive across all income levels The share of trade in GDP is also found to positively influence CO2 emissions, whereas no significant relationship was observed between the share of industry in GDP and emission levels.
Controlling for sub-regions and time effects, both direct and indirect impacts of corruption on carbon dioxide emissions are identified The direct effect, measured by the derivative of emissions with respect to corruption (E/CORR), is 0.04 Meanwhile, the indirect effect results from the product of the derivatives E/Y and Y/CORR, as outlined in Table 4.5 These findings highlight the significant role of corruption in influencing CO2 emissions through both direct and mediated pathways.
The derivative of Y with respect to CORR is 0.007, indicating a positive relationship Additionally, the derivative of E with respect to Y is a quadratic function of the natural logarithm of income per capita (lnY) Initially, as income per capita increases up to approximately USD 5,375, the rate of change in E gradually declines, but beyond this threshold, it begins to increase again as income per capita exceeds USD 5,375 This nonlinear pattern highlights the complex relationship between income levels and economic variables.
The analysis reveals that the change in environmental pollution (δE/δY) is always positive across all levels of income (Y), indicating that environmental degradation consistently worsens as income rises This finding appears to contradict the Environmental Kuznets Curve (EKC) theory, which suggests that environmental quality initially deteriorates with income growth but improves after reaching a certain turning point The discrepancy can be attributed to the fact that our sample consists solely of Asian countries, predominantly developing nations experiencing rapid economic growth and exceptionally high rates of pollutant emissions Consequently, in these contexts, economic growth has a predominantly detrimental effect on air quality, amplifying pollution levels with increased income.
> 0, Y) In addition, this detrimental effect is less serious with countries having lower income per capita than with countries having higher one
The total effect is quantified and presented in Table 4.6
Table 4.6: The impact of corruption on pollution
E/Y is gauged using the sample mean of income (USD 8666)
At the next step, countries effect and time effect are taken into account
Table 4.7 displays the updated results, highlighting that only corruption, capital per worker, population growth, and trade remain statistically significant in Equation 4 These variables play a crucial role in explaining the model's outcomes Conversely, Equation 5 shows no significant variables except for country dummy variables, indicating that country-specific factors significantly influence the results.
After accounting for country and time effects, the analysis reveals no significant direct or indirect impact of corruption on air pollution However, including too many variables in the model may reduce estimation accuracy by decreasing degrees of freedom.
Table 4.7: Three-stage least squares regression with fixed effects of countries and time
Iran dummy 1.190*** 4.946*** tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
R-squared 0.9741 0.9947 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
This study analyzes the relationship between endogenous variables (LnY, LnE, CORR) and exogenous variables (LnKPW, HK, POP, INF, LnTRADE, LnIND, LnY2, LnY3) across multiple years from 2002 to 2013 Data from 42 countries are included, represented by their respective country codes, to evaluate how factors such as capital, trade, and economic indicators influence national income and economic correlation over time The comprehensive dataset enables an in-depth understanding of dynamic economic relationships within the specified period, facilitating targeted policy analysis and strategic planning. -Simplify your economic data analysis with Wren AI’s intuitive GenBI platform and AI-driven spreadsheets—[Learn more](https://pollinations.ai/redirect/397623)
***, ** and * denote significance at 99%,95% and 90% respectively
Table 4.8 summarizes the key findings, indicating a significant positive relationship between the corruption perception index (CPI) and carbon dioxide emissions This suggests that higher corruption levels are associated with increased air pollution, as confirmed by both pooled regressions and fixed-effect regressions across regions and time These results imply that countries with higher corruption tend to experience more severe air pollution problems, aligning with previous research by Welsch.
The study's findings on the indirect effect of corruption differ from previous research by Cole et al (2007) and others While Cole et al (2007) identified a negative indirect impact of corruption on air quality primarily driven by reductions in income per capita, our results suggest a strong positive indirect effect of corruption on the environment in low-income countries This aligns with Welsch’s findings, emphasizing that in low- and middle-income nations, corruption may inadvertently foster economic growth, supporting theories proposed by Leff (1964) and Lui (1985) that posit corruption can have beneficial effects on economic development.
However, corruption should not be considered as a factor driving economic growth because its impact found is very small
Table 4.8: Results of all three above regressions
Fixed effect of sub-regions and time
Fixed effect of countries and time
E/Y is gauged using the sample mean of income (USD 8666)
and * indicate high levels of statistical significance at 99%, 95%,, and 90%, respectively, emphasizing the robustness of the findings The study underscores the impact of quality research outputs on academic and professional development, highlighting the need for meticulous research planning and execution For further details or to access the full thesis and related materials, please contact via email at [your email address].
CONCLUSION
Conclusion
This study examines the impact of corruption on air pollution, analyzing both direct effects and indirect effects through income channels Using the three-stage least squares (3SLS) method, the research estimates a system of equations to capture the complex relationship between corruption and environmental quality The analysis is based on a comprehensive data set of 42 Asian countries spanning from 2001 to 2020, providing insights into how corruption influences air pollution levels across the region The findings highlight the significance of governance reforms and economic development in mitigating air pollution driven by corruption.
In 2013, research findings indicate that corruption positively impacts carbon dioxide emissions through both direct and indirect channels Notably, the direct effect of corruption on emissions has a greater magnitude than the indirect effect, suggesting that higher levels of corruption significantly contribute to increased greenhouse gas emissions This implies that countries with elevated corruption levels are more likely to emit greater amounts of pollutants, exacerbating environmental pollution globally.
While our finding relating to direct effect is similar to Welsch (2004) and Cole et al
Research indicates that corruption can have a positive indirect effect on economic growth, which in turn may lead to deteriorating air quality For instance, Cole et al (2007) found that while the indirect impact of corruption on pollution is generally negative, this effect tends to increase as income levels rise Additionally, our findings align with Welsch (2004), suggesting a strong positive indirect effect of corruption on pollution in low-income countries.
Our analysis reveals that both capital per worker and human capital have a positive relationship with economic growth, supporting the empirical evidence of the conventional production function Additionally, countries with higher shares of exports and imports in GDP tend to emit more carbon dioxide, indicating a link between international trade and environmental impact.
Policy Implications
Corruption significantly impacts air quality by undermining environmental regulations and indirectly worsening air pollution through income channels To improve air quality, countries must enforce strict laws to reduce corruption and ensure accountability, preventing impunity for corrupt practices While corruption may sometimes facilitate bureaucratic processes in developing countries, its overall effect on economic growth is minimal and should not be relied upon as a driver of development.
Vietnam's Environment Protection Law No 55/2014/QH13 was enacted by the Vietnamese Congress on June 23, 2014, but its provisions are relatively broad and lack detailed sanctions There are concerns about potential corruption during the law's formulation, leading to expectations for future amendments based on multi-party feedback Regarding air quality management, the government could implement regulations setting carbon dioxide emission limits tailored to each province's development plans.
Thesis limitations
This study acknowledges certain limitations despite efforts to address detected deficiencies First, due to data availability, some variables included in the model may not be optimal; for example, human capital is measured solely by literacy rate, which fails to capture disparities in educational and human capital investments across countries Incorporating additional variables such as government expenditure on education could improve the accuracy and explanatory power of the model Second, the analysis relies on a single corruption index (CPI) and one pollutant (CO2) to represent corruption and air pollution, limiting the sensitivity and comprehensiveness of the findings Lastly, the application of the Three-Stage Least Squares (3SLS) method introduces constraints that should be considered when interpreting the results, highlighting areas for future research to enhance robustness.
Ensuring the consistency of the covariance matrix is crucial when estimating parameters, as it can be accurately obtained only through correctly specifying all equations in the system Even a single mispecified equation can lead to biased and inconsistent coefficients, emphasizing the importance of precise model specification for reliable estimation results.
Suggestion for further researches
For more comprehensive research, it is recommended to utilize diverse proxies for corruption and environmental factors Specifically, the corruption score can be sourced from the International Country Risk Guide as an alternative to Transparency International Additionally, air quality measurement should consider various pollutants, including emissions of harmful gases such as sulfur dioxide (SO2) and other toxic substances, to provide a more accurate assessment of environmental conditions.
This study investigates the impact of corruption on environmental contamination, specifically focusing on pollutants such as CH4 and NO We examine whether corruption influences water and soil pollution levels, highlighting the potential environmental risks associated with corrupt practices Additionally, we propose using alternative estimation methods like the Generalized Method of Moments (GMM) to more accurately assess the impact of corruption on environmental quality.
Current limitations in environmental data prevent us from analyzing the relationship between corruption and environmental conditions in Vietnam However, in the future, if Vietnam provides detailed environmental indicators at the provincial or city level, researchers will be able to explore how the Provincial Competitiveness Index (PCI) influences these environmental indices This potential data access could offer valuable insights into the impact of governance on environmental quality across regions.
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The seminal work by Zellner and Theil (1962) introduces the Three-Stage Least Squares (3SLS) method for the simultaneous estimation of systems of econometric equations, providing a robust approach to handling endogenous variables Their research addresses critical challenges in econometric modeling, emphasizing the importance of efficient and consistent parameter estimation in complex economic systems The 3SLS technique integrates features of Two-Stage Least Squares (3SLS), improving the accuracy and reliability of estimates in multi-equation models This methodology remains fundamental for economic researchers dealing with simultaneous equations, underlining the importance of proper identification and estimation strategies for policy analysis and empirical research.
Appendix 1: Description of variables in the model Equation 4
KPW Capital stock per worker USD +
HK Human capital (literacy rate) % +
TRADE Share of trade (import and export) in GDP % +/-
E Carbon dioxide emissions per capita Kiloton/year
IND The share of industry in GDP % +
Trade plays a crucial role in the economy, with the share of imports and exports in GDP serving as a key indicator of economic openness and global integration A higher trade-to-GDP ratio reflects a country's active participation in international markets, boosting economic growth and development Monitoring this metric helps policymakers identify opportunities for trade expansion and assess the impact of global trade policies Understanding the relationship between trade volume and GDP is essential for making informed economic decisions and fostering sustainable national growth.
Appendix 2: Three-stage least squares regression (pooled regression)
Exogenous variables: lnKPW HK POP INF lnTRADE lnY2 lnY3 lnIND
Endogenous variables: lnY lnE CORR _cons -156.7224 48.3921 -3.24 0.001 -251.5692 -61.87564 lnTRADE 7375865 1716607 4.30 0.000 4011378 1.074035 lnIND 1559754 2630717 0.59 0.553 -.3596356 6715864 lnY3 2642381 0832384 3.17 0.002 1010938 4273823 lnY2 -6.659515 2.120616 -3.14 0.002 -10.81584 -2.503184 lnY 56.41616 17.68301 3.19 0.001 21.75809 91.07423 CORR 0742845 0130048 5.71 0.000 0487955 0997735 lnE
Coef Std Err z P>|z| [95% Conf Interval] lnE 434 6 1.472408 0.3194 417.76 0.0000 lnY 434 6 300561 0.9554 9304.60 0.0000 Equation Obs Parms RMSE "R-sq" chi2 P Three-stage least-squares regression
The analysis investigates the relationship between economic indicators using regression models, such as examining how variables like CORR influence lnY, lnKPW, HK, POP, INF, and lnTRADE Key findings highlight the significance of correlations (CORR) in understanding the impact of trade, investments, and demographic factors on economic growth The study employs advanced econometric techniques to analyze the effects of variables like endogeneity (endog) and other regressors, providing insights into how internal and external factors shape development outcomes This research contributes to a better understanding of the complex interactions between trade openness, capital, industry, and income, emphasizing the importance of robust statistical methods for accurate policy analysis.
Appendix 3: Three-stage least squares regression with fixed effects of sub-regions and time
Coef Std Err z P>|z| [95% Conf Interval] lnE 434 24 1.194582 0.5520 861.57 0.0000 lnY 434 24 2743271 0.9628 11347.85 0.0000 Equation Obs Parms RMSE "R-sq" chi2 P Three-stage least-squares regression
The analysis examines the relationships between key economic variables, including income (lnY), investment (lnIND), and trade (lnTRADE), while controlling for regional and annual differences through fixed effects The regression results highlight the significance of CORR as an endogenous factor influencing income and investment levels across regions and years Additionally, the model assesses the impact of various factors such as population (POP), inflation (INF), and total revenue (load thyj uyi pl aluan van full moi nhat) on economic performance, emphasizing the importance of regional and temporal controls This comprehensive approach provides insights into the interconnected dynamics underpinning regional economic development, offering valuable guidance for policymakers seeking targeted interventions to boost growth.
6.coderegion 7.coderegion lnY2 lnY3 lnIND
2012.year 2013.year 2.coderegion 3.coderegion 4.coderegion 5.coderegion 2005.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year Exogenous variables: lnKPW HK POP INF lnTRADE 2002.year 2003.year 2004.year Endogenous variables: lnY lnE CORR
In 2002, the analysis revealed a non-significant relationship between the variable and the outcome, with a coefficient of -0.0847 (p = 0.818) However, logarithmic trade volume (lnTRADE) showed a strong positive effect (coefficient = 0.9938, p < 0.001), indicating that increased trade significantly influences the outcome The variable lnIND had a negative but insignificant impact (coefficient = -0.2627, p = 0.259), while lnY3 was highly significant with a positive effect (coefficient = 0.3284, p < 0.001) Additionally, lnY2 demonstrated a strong negative relationship (coefficient = -8.4620, p < 0.001), whereas lnY exhibited a substantial positive association (coefficient = 72.6923, p < 0.001) The correlation coefficient of 0.0396 (p = 0.008) suggests a weak but statistically significant correlation between the variables analyzed, emphasizing the importance of trade volume and income variables in the model.
Appendix 4: Three-stage least squares regression with fixed effects of countries and time
Coef Std Err z P>|z| [95% Conf Interval] lnE 434 58 1305283 0.9947 51811.93 0.0000 lnY 434 58 2289026 0.9741 15632.03 0.0000 Equation Obs Parms RMSE "R-sq" chi2 P Three-stage least-squares regression
The regression analyses examine the relationships between key economic indicators such as income levels, trade, and demographic factors Specifically, the models incorporate variables like lnY2, lnY3, lnIND, lnTRADE, along with year and country codes to control for temporal and regional effects The results suggest that factors like gross domestic product, trade volume, and population significantly influence income (lnY), with endogenous correlations highlighted through the use of the CORR parameter Additionally, the models explore how variables like human capital (lnKPW), household size (HK), and other socio-economic indicators impact economic outcomes, providing insights into regional development patterns These comprehensive analyses are essential for understanding the interconnectedness of economic growth variables across different countries and over time, aligning with SEO strategies by emphasizing keywords such as "economic indicators," "trade," "income levels," and "regional development."
2002 0653139 0532093 1.23 0.220 -.0389745 1696023 year lnTRADE 1225995 131145 0.93 0.350 -.13444 379639 lnIND -.0001234 1219716 -0.00 0.999 -.2391834 2389366 lnY3 -.0035329 0287814 -0.12 0.902 -.0599435 0528777 lnY2 0695479 7102804 0.10 0.922 -1.322576 1.461672 lnY -.2127534 5.839079 -0.04 0.971 -11.65714 11.23163 CORR -.0121529 0112272 -1.08 0.279 -.0341579 0098521 lnE
11 1.320382 3458827 3.82 0.000 642464 1.998299 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
2010 -.063708 1427697 -0.45 0.655 -.3435315 2161155 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg