The effect of corruption on economic growth in southeast asia countries

INTRODUCTION

Research problem

In recent decades, researchers have sought to understand the key determinants of economic growth, exploring why some nations achieve rapid long-term income growth while others do not This inquiry stems from the ongoing debate over the primary drivers of growth, given the multitude of factors influencing economic development Two prominent theories have emerged: the neoclassical growth theory by Solow, which emphasizes the importance of investment, and the endogenous growth theory by Romer and Lucas, which highlights labor and innovation capacity These theories have sparked extensive discussions about the essential sources of growth, with some scholars pointing to labor, capital, technological progress, and trade openness, while others emphasize the roles of institutions, democracy, the rule of law, geographical factors, and corruption.

Economists have long debated the impact of corruption on economic growth, with notable studies by Left (1964), Becker (1968), Lui (1985), and others Despite extensive research, a consensus on the relationship between corruption and economic development remains elusive Some studies suggest that a certain level of corruption can positively correlate with economic growth, particularly in countries with complex and opaque administrative processes In such environments, corruption may facilitate quicker economic operations, such as obtaining licenses or completing construction projects, thereby saving time for businesses (Lui, 1985) This concept is encapsulated in the "efficient grease" hypothesis, which posits that corruption can enhance economic efficiency by allowing firms to access capital at a lower cost through bribery.

In emerging countries with free open market economies, applicants often resort to corrupt overpayments to bureaucrats for enterprise licenses they rightfully deserve, as highlighted by Manion (1996) This practice allows bureaucrats to boost their income, leading to increased spending on goods and services, which in turn stimulates economic growth However, underdeveloped nations struggle to combat corruption without first attaining a significant level of economic development Failing to do so results in substantial investments of labor, time, and financial resources by the government in efforts to mitigate corruption.

Numerous empirical studies, including those by Mauro (1995, 1996), Pak Hung Mo (2001), and Pellegrini and Gerlagh (2004), demonstrate a significant negative correlation between corruption and economic growth after accounting for key economic determinants such as government expenditure and political instability Mohamed Dridi (2013) utilized the Channel Methodology on data from 82 developed and developing countries (1980-2002) and found that an increase of one point in the corruption index leads to a 0.9% decrease in annual growth rates, primarily through human resources, government spending, and political conditions Dridi's research highlights human resources and political stability as critical channels for corruption's detrimental impact on growth In contrast, Mo (2001) identified that political instability accounts for 53% of the total corruption effect on economic growth using the decomposition method.

(1996) showed the strong negative effects of corruption on growth in 94 countries with observations period from 1960 to 1985 Mauro (1996) concluded that

Corruption significantly hampers investment, particularly in the private sector, and leads to decreased government spending on education, which is crucial for growth Research by Kaufmann and Wei (1999), utilizing data from the Global Competitiveness Report Surveys of 1996 and 1997 and based on Stackelberg game theory, examined the link between bribery and bureaucratic inefficiency Their findings did not support the "speed money" hypothesis, effectively refuting the "efficient grease" thesis, and concluded that corruption negatively impacts economic growth.

The relationship between corruption and economic growth remains uncertain, as highlighted by various studies (Andvig and Moene, 1990; Ehrlich and Lui, 1999; Aidt et al., 2008; Haque and Kneller, 2009; Leite and Weidmann, 1999) Haque and Kneller (2009) utilized a Threshold model analyzing data from 54 developed and developing countries between 1980 and 2003, revealing a strong negative correlation between corruption and economic growth They noted that this relationship is influenced by the cultural context of corruption, with shifts in culture potentially disrupting this dynamic Furthermore, as the majority of resources are state-owned, government focus on corruption can exacerbate development traps Similarly, Ehrlich and Lui (1999) identified a non-linear relationship between corruption and economic growth.

This thesis explores the relationship between corruption and economic growth by reviewing key theoretical and empirical studies It specifically re-evaluates the effects of corruption on economic growth in Asian countries using diverse econometric methods A new dataset is compiled and analyzed through the formulation of economic growth equations that incorporate potential transmission variables related to corruption The findings aim to provide additional evidence on the causal relationship between corruption and economic growth.

Research objectives and research questions

This paper mainly focuses on the core objective is re-examining the effects of corruption on economic growth in Asian countries for the period from 1996 to

2014 This thesis tries to understand, identify, and explain how corruption will affect economic growth directly and indirectly via transmition channels in the sample.

As above mentioned, this thesis tries to address the relationship between corruption and economic growth Therefore, the questions should be raised as follows:

- Does corruption impact (promote or impede) the economic growth in Asian countries directly and indirectly?

- How are corruption effects on economic growth via five possible transmission channels: investment, human capital, political instability, government expenditure, and openness to trade.

Thesis Structure

This thesis is divided into five chapters.

Chapter 1: Introduction presents numerous conclusions about the correlation between corruption and economic growth from various theoretical and empirical researches Besides, it explains what vital objectives are, what research questions should be raised to answer in this paper.

Chapter 2: Literature review provides a general review of economic theories associated to the causal correlation between corruption and economic growth This part describes theoretical framework and empirical studies consistent with this issue In addition, the definitions of the variables used in the model are mentioned in this chapter.

Chapter 3: Methodology is concerned about the general methods and how to check effects of corruption on economic growth directly and indirectly, which mainly involves in the following points:

- analytical framework for the problem to be investigated

- appropriate models with variables to be described for the issue

- data sources and sample to be used to estimate the existence the corruption – economic development relationship

- research methodology and technique for processing and analyzing data set

Chapter 4: Empirical Findings focuses on presenting the estimation results about influences of corruption on growth.

Chapter 5: Conclusions, Limitation and Future research comes to the main findings achieved in the previous chapter Accordingly, this part points out the limitations and further studies concerning the relationship between corruption and economic growth

LITERATURE REVIEW

Theoretical concepts related to economic growth and corruption

Economic growth is an expansion in productive capacity of commodities and services, and average national income level in a period of time, compared with an another period (Perkins et al., 2006, p 12)

When comparing the economic growth of two countries, Gross Domestic Product (GDP) and Gross National Product (GNP) are commonly used metrics These indicators can be calculated in nominal terms, which accounts for inflation, or in real terms, which adjusts for inflation to provide a clearer picture of economic performance.

While there is no single prevailing theory of economic growth, most research indicates that variations in income levels between countries can be attributed to differences in factor endowments, productivity, technology, or a combination of these elements.

In his seminal 1956 growth theory, Solow discovered that capital's contribution to economic growth is significantly lower than anticipated, resulting in a substantial unexplained residual, despite accounting for effective labor.

Following Solow's research, growth theory diverged into two main paths One path questioned the validity of Solow's aggregate production function, with Lewis (1954) notably proposing a dual sector model that illustrated economic growth through the labor transition from traditional agriculture to modern industry The other path explored the substantial impact of exogenous technical change, known as the Solow residual, which was found to differ significantly from theoretical predictions Economists in this direction emphasized the interplay of political and economic factors to explain variations in growth rates.

Notable contributions to endogenous growth theories include works by Lucas (1988), Romer (1989), and Mankiw, Romer, and Weil (1992), which aimed to adjust labor for quality These theories expanded upon the neoclassical production function established by Solow by incorporating variables such as human capital and research and development (R&D) Specifically, the cross-sectional study by Mankiw, Romer, and Weil (1992) demonstrated that including human capital accounted for approximately eighty percent of the variation in growth rates.

Endogenous growth theory has highlighted key aspects of the Solow residual, but Robert J Barro and his colleagues (1991, 1995) identified additional factors that serve as benchmarks for future empirical research They examined the significance of human resources, government policies, institutional quality, trade openness, individual freedoms, development aid, and financial reforms.

In term of definition, corruption is a complex phenomenon Generally, corruption is the misuse of entrusted authority for extra positive personal benefit (Transparency International website).

Corruption can be categorized into three main types: grand corruption, bureaucratic corruption, and political corruption These classifications are based on the specific sector in which the corruption occurs and the amount of money involved in the illicit transactions.

Grand corruption, synonymous with political corruption, involves high-level activities that distort government policies and undermine the central operations of a nation This form of corruption is centralized, impacting all citizens and eroding trust in public institutions.

Bureaucratic corruption, often referred to as petty or low-level corruption, involves the small payments made by ordinary citizens to public officials in order to access essential services such as healthcare, education, law enforcement, and licensing This form of corruption, also known as street-level corruption, is characterized by its decentralized nature, impacting everyday interactions with various government agencies.

16 the exact bribes taken are not arranged It just makes bureaucrats speed up the procedure or skip legal penalties.

Political corruption occurs when decision-makers exploit their authority to manipulate policies and public resource allocation, ultimately enhancing their power and wealth This form of corruption significantly affects election outcomes and the actions of legislators.

The most widely used corruption measurement tools in empirical research include the Corruption Perception Index (CPI), Bribe Payers Index (BPI), Global Corruption Barometer (GCB), and National Integrity System assessments (NIS) recorded by Transparency International, as well as the Control of Corruption Index (CC) from the Worldwide Governance Indicator group and the Corruption Index (CI) from the International Country Risk Guide This study will utilize the CPI, which, despite its popularity, has limitations as it primarily reflects perceptions of corruption within public sectors, specifically in administrative and political categories.

Effect of corruption on economic growth: theoretical literatures

Corruption negatively impacts economic output by influencing capital, labor, and various other factors, as illustrated by rent-seeking, queue models, and transaction cost theory It diminishes productive work, leading to a decline in actual wealth creation, a loss of government revenue, and increased income inequality due to the inefficient allocation of resources, including both capital and labor.

Rent seeking is a crucial concept related to corruption, first introduced by Gordon Tullock in 1967 and later popularized by Anne Krueger in 1974 According to Klostad and Soreide (2009), individuals and groups engage in rent seeking when they pursue additional profits through political means rather than utilizing their time and skills effectively.

Bade and Parkin (2013) argue that rent-seeking activities, such as bribery and lobbying, aim to extract benefits from international trade, leading to profit accumulation This culture of rent-seeking, characterized by tariffs, import restrictions, and quotas, results in resource misallocation and deadweight loss, diminishing social benefits Consequently, higher prices lead to reduced output, which ultimately hampers economic growth.

Queueing theory, pioneered by Agner Krarup Erlang in 1909, is designed to predict queue lengths and waiting times for services across various sectors, including businesses, shops, offices, and hospitals Lui (1985) expanded on this model to demonstrate how bureaucrats may expedite the issuance of business licenses to firms, often favoring those who engage in corrupt practices, such as bribery This indicates that entities seeking to establish businesses or factories may experience reduced paperwork and quicker license approvals through unethical means, suggesting a positive correlation between corruption and economic growth rates.

According to Lambsdorff (2002), the transaction costs associated with legal contracts encompass expenses related to finding partners and gathering information, negotiating terms, and ensuring compliance with contract conditions In cases of corrupt agreements, these costs often remain hidden, leading to the involvement of brokers in the discussions of such contracts.

Lambsdorff (2002) highlights that the presence of parochial corruption in a goods and services market leads to increased total transaction costs These costs arise from the need to invest more resources in finding reliable partners, assessing quality, evaluating product and individual capabilities, and dealing with the implications of corrupted goods The search for potential partners typically ceases when the expenses incurred reach the marginal transaction cost associated with this search.

LE KIM Page one additional partner, which is equal to the estimated profit generated from a potentially superior dealing with another competitor.

Higher marginal transaction costs lead to a reduced search for potential collaborators, enabling entrepreneurs to conserve capital and redirect investments into other projects As these projects progress, they facilitate the hiring of additional workers, ultimately boosting productivity in goods and services, which contributes to overall economic growth.

Effect of corruption on economic growth: empirical studies

There are two primary methods for analyzing the effects of corruption on economic growth: the single equation approach and the system of equations approach The single equation approach incorporates a corruption variable into the growth function to estimate its overall impacts, while the system of equations approach employs interrelated equations to distinguish between the direct and indirect relationships between corruption and growth.

Corruption is a global issue that transcends national boundaries, affecting both wealthy and impoverished nations, regardless of their political or economic systems Economists continue to explore the relationship between corruption and economic growth, yet no consensus has emerged Some researchers argue that a moderate level of corruption can positively influence a country's development For instance, Leff's "grease the wheel" hypothesis suggests that corruption can enhance economic growth by alleviating government-imposed constraints and facilitating beneficial economic decisions Similarly, Lien's Competitive Bribery Game further examines these dynamics.

(1985) with Equiblirium queuing model also suggested that corruption may be enhance economic growth.

Concerns regarding the detrimental effects of corruption on economic growth have surged in both developing and developed nations Numerous empirical studies, such as those conducted by Mauro (1995) and Tanzi, have highlighted this critical issue.

(1998), Aidt (2003), Akai N et al (2005), Boris P et al (2008) These studies

Private firms that secure government contracts through high payments may not be economically competitive, leading to inefficient use of human resources that can hinder economic growth Additionally, smaller, emerging companies often face significant challenges due to the necessity of making side-payments to corrupt government officials, which can stifle their potential as a driving force for economic development.

Kwabena Gyimah-Brempong (2002) analyzed a dataset from 21 African countries spanning 1993 to 1999, modifying the economic growth equation to include corruption as an independent variable The linear growth equation is expressed as g = α₀ + α₁k + α₂edu + α₃x + α₄corrupt + α₅y₀ + α₆govcon + εᵢ, where g represents the rate of economic growth of real income, εᵢ is the stochastic error term, and αᵢ are the coefficients to be estimated The explanatory variables include k (investment rate), edu (educational attainment of the adult population), x (growth rate of real exports), corrupt (corruption), govcon (government consumption), and y₀ (initial level of income).

Employing OLS regression method, Kwabena Gyumah-Brempong (2002) concluded that corruption affects economic growth negatively and significantly.

Mauro (1995) analyzed the relationship between corruption and GDP per capita growth rates from 1960 to 1985 using data from Summers and Heston (1988) and Business International’s 1984 corruption index His findings indicated a significant negative correlation between the standard deviation of the corruption index and annual GDP per capita growth when using instrumental variables like ethno-linguistic fractionalization However, this correlation was not robust without control variables Once factors such as secondary education in 1960, government expenditure, assassinations, investment, and political instability were included in the analysis, the impact of corruption on economic growth became insignificant.

Nwankwo (2014) also employed Johansen co-integration test, granger-causality test, Unit root test, Error correction mechanism and OLS method to check only the simple econometric model below:

Linear function: GDP = b0 + b1 * COR + Ut and Log function: Log (GDP) = b0 + b1log(COR) + Ut

A study by Nwankwo (2014) analyzing data from Nigeria between 1997 and 2010 reveals a significant long-term relationship between the corruption index (COR) and the gross domestic product (GDP) The findings indicate that higher levels of corruption adversely affect Nigeria's economic growth, with a negative correlation coefficient of b1 = -4.680.

In summary, research on the direct impact of corruption on economic growth is limited, as corruption influences growth through various channels, including investment, education, international trade, political stability, and government spending Therefore, employing a system of equations approach is essential to accurately assess the overall effect of corruption on development.

2.3.2 The system of equations approach.

Numerous studies, including those by Pak Hung Mo (2001) and Pellegrini and Gerlagh, have demonstrated that corruption adversely affects economic growth By utilizing a system of equations approach, researchers have been able to assess both the direct and indirect impacts of corruption on the economy, reinforcing the consensus that corruption significantly hinders economic development.

(2004), Lorentzen, Mc Milan and Wacziarg (2008), Pellegrini (2011), Marie Chene

(2014), Campos et al., (1999), Mohamed Dribi (2013) The typical system of equations is specified as below:

Direct effect: GDP = f(CPI, TV) (named as equation 1a, 1b)

Indirect effects: TV = f(CPI, GDP) (named as equation 2a,

2b) where TV presents other determinants of economic growth

2.3.2.1 Direct effects of corruption on economic growth

Pellegrini and Gerlagh (2004) modeled the direct relationship between economic growth and corruption as follows:

The dependent variable, G i, denotes the annual GDP growth rate from 1975 to 1996, calculated as G i = (1/T)ln(YT / Yo) The independent variable is represented by the logarithm of the initial income level, Yo i, which has a negative coefficient, indicating that α1 < 0.

C i : explanatory variable, represented by corruption which measures the extent of bribes and bribes asking in one country from 1980 to 1985 Data are from the Corruption Perception Index by Transperancy International.

Z i : vector of other independent variables: investment, schooling, trade openness, political instability (Levine and Renelt, 1992; Sachs and Warner, 1995)

Investment: The percentage of gross public investment and private investment on GDP Then they calculated an average for the whole period 1975-1996 Data are from Penn World table 6.0.

Schooling: The average years of schooling of all residents over 25 years old in 1975 Data are taken from the International Data on Education Attainment by Barro and Lee

Trade Openness: The number of years opened for trade of each country over the period 1965-1990.

Between 1970 and 1985, the average number of revolutions and assassinations per million residents annually was analyzed, utilizing data from the Natural Resource Abundance and Economic Growth database The findings include an error term, denoted as ε i, to account for potential discrepancies in the data.

LE KIM Page i, t : Country i and time t respectively

Pellegrini and Gerlagh (2004) analyzed a dataset of 48 countries from 1965 to 1996, revealing a negative coefficient of 0.07 for corruption, indicating that its direct impact on economic development is negligible and statistically insignificant Similarly, Pak Hung Mo (2001) explored the relationship between corruption and economic growth, contributing to the ongoing discussion on the effects of corruption on development.

GR = F (CORRUPT, y70, PRIGHT, PRIGHT 2 , HUMAN, INSTAB, IY, POPG)

GR : Growth rate of real GDP in percentage

CORRUPT : Corruption index y70 : The initial per capita of national income

PRIGHT : The Gastil index of political rights

HUMAN : Average years of schooling in the total residents over the age of twenty five from 1970 to 1985 INSTAB : Measure of political instability

IY : Ratio of private investment to GDP

POPG : Rate of population of one country growth

A panel dataset analyzed by Robert Barro and Jong-Wha Lee from 1960 to 1985 revealed that corruption has an insignificant negative impact on output volume, as indicated by the OLS regression results from Mo (2001) The study found a negative coefficient for corruption, measuring only 0.06459.

2.3.2.2 Indirect effects of corruption on economic growth – Channel of Transmission

This research examines the indirect effects of corruption on economic growth through five key transmission channels: political instability, investment, human capital, government expenditure, and trade openness.

Mo (2001) suggested the below formula to compute the influence of corruption on growth via plausible elements.

(equation 2b) in which TV represents each of three possible transmission channels namely investment, human resources, and political instability.

While Pellegrini and Gerlagh (2004) modeled the indirect relationship between economic growth and corruption through channels as follows:

Corruption significantly affects key dependent variables such as investment, education, international trade openness, and political stability, while the initial income level is represented by the logarithm of the degree of income (ln(Yo i)).

C i : described by corruption βo, β1, β2 : four-dimensional vectors of coefficients à i : denoted as the vector of residuals

To control endogeneity of the corruption variable through 2SLS regression, Mo

METHODOLOGY AND DATA

General methods

3.1.1 Conceptual framework for the study

Figure 1 : The effect of corruption on economic growth

Corruption perceptions index (CPI) (-) (DIRECT EFFECT)

This study is based on the Solow growth model, which utilizes a production function where output is determined by capital (K), labor (L), and the Solow residual (A) Empirical research indicates that factor accumulation accounts for only about half of output growth variation, with the remainder attributed to the Solow residual, highlighting unknown determinants of economic growth Therefore, this study will not only consider traditional variables like capital and labor but will also examine significant factors identified in previous research, including corruption, political conditions, government expenditure, and trade openness The paper aims to assess how much of the variation in economic development can be explained by factor accumulation and key determinants of the Solow residual, with a particular focus on the direct and indirect effects of corruption on economic growth.

While Mauro (1995), Acemoglu and Verdier (1998), Mo (2001), Blackburn et al

Research indicates a negative correlation between per capita wealth growth and corruption (Aidt, 2009) However, earlier studies by Lui (1985), Lien (1986), and Leff (1964) suggest that corruption can sometimes promote economic efficiency and growth Additionally, Ehrlich and Lui (1999) identified a non-linear relationship between corruption and economic development This framework aims to clarify the direct effects of corruption on economic development in Asian countries, revealing potential outcomes that may be positive, negative, or uncertain.

Corruption significantly affects a country's development, influencing economic growth through five key channels: investment, human capital, political instability, government expenditure, and trade openness.

In terms of the first channel – investment, corruption increases transaction costs and asymmetric information so reduces the motivation to invest The decrease in the

Capital accumulation negatively affects national income, as highlighted in this study A reduction in investment leads to diminished growth and benefits for both entrepreneurs and individuals The Solow growth model underscores the significance of investment in production factors—capital, labor, and technological progress This research will examine the influence of corruption on growth through investment, utilizing gross domestic fixed investment as a proxy for capital due to the lack of reliable capital stock data in many countries However, previous studies, such as Bosworth and Collins (2003), indicate that using investment ratios as a capital proxy yields unsatisfactory results Consequently, this research will adopt Rodney Smith's (2010) methodology to create a capital stock series based on the World Bank's gross fixed capital formation indicator.

(1 )K t 1 GFK t and K 0 GFK 0 / (g GFK ) Where GFK is gross fixed capital formation, g GFK is the rate of growth in gross fixed capital formation, is the depreciation rate (0.05 in this study).

Human capital is a crucial driver of economic growth, as it encompasses the skills acquired by labor through education, training, and experience, significantly influencing technology levels and productivity Research by Barro (1991), Mankiw et al (1992), and Barro and Sala-i-Matin (1995) highlights the importance of skilled labor in economic development, demonstrating that higher rates of schooling and education correlate with increased production of goods and services, leading to faster national development However, corruption undermines productivity by prioritizing personal gain over collective benefit and reduces investment in education, further hindering economic progress.

In the other words, corruption has a negative consequence on growth transferred via

The LE KIM Page focuses on human capital, using secondary school enrollment rates as a proxy due to the infrequency of average years of schooling data collection, which occurs every five years.

Political instability significantly impacts the relationship between corruption and economic growth, as it is influenced by factors such as equity, income disparity, and violence levels During periods of heightened political instability, corruption tends to create a detrimental environment for economic performance, indicating a positive correlation between corruption and political instability.

Corruption significantly hampers economic growth by distorting government expenditure on critical sectors such as infrastructure, education, and healthcare When governments misuse their authority to inflate budgets for personal gain, it results in inefficient allocation of funds, ultimately undermining national development Conversely, some argue, like Daniel J Mitchell (2005), that increased government spending can stimulate economic growth by injecting more capital into the market This paper will analyze government final consumption expenditure as a proxy to assess the impact of corruption on economic growth.

Trade openness is a crucial determinant of economic growth, particularly in developing countries Research indicates that nations with higher levels of trade openness tend to achieve greater GDP per capita and experience faster growth, as demonstrated by studies from Karras (2003), Robert and David (1999), and Dollar (1992) This openness facilitates technology and knowledge transfer, enhances economies of scale, and influences economic development by impacting corruption through quotas, taxes, and restrictions on exports and imports, as noted by Pellegrini and Gerlagh.

(2004) illustrated that corruption and trade openness has a negative relationship.

Following the approach employed by previous researches, this study will use the ratio of exports plus imports to output as the proxy for openness to trade.

The hypotheses are intended to test in this research are follows:

- Hypothesis 1: Corruption has a negative impact on economic growth.

- Hypothesis 2: Corruption has a negative correlation with economic growth indirectly via investment channel.

- Hypothesis 3: Human capital is the vital channel through which corruption is likely to decrease economic development.

- Hypothesis 4: Political instability is an important channel via which corruption operates to reduce growth.

- Hypothesis 5: Government expenditure is one of the channels that involves a positive contribution of corruption on national income.

- Hypothesis 6: Corruption negatively contribute to economic growth via transmission channel – Openness to trade The expected signs of variables are described in Table 1.

Table 1: Expected sign of selected variables:

Corruption through Negative Positive (+) Mohamed Dribi (2013)

Mohamed Dribi (2013) Pak Hung Mo (2001) Pellegrini and Gerlagh(2004)

Mohamed Dribi (2013) Wouter Ebben and Albert de Vaal (2009) Pak Hung Mo (2001) Pellegrini and Gerlagh(2004) Corruption through

Mohamed Dribi (2013)Pellegrini andGerlagh(2004)

Research models and econometric methodology

Based on the theoretical arguments summarised as aboved, this research mainly investigates the impacts of corruption on economic growth directly and indirectly

LE KIM Page via five possible transmission channels: investment, human capital, political instability, government expenditure, and openness to trade

With the objectives as presented in this paper, the system of estimated regression equations on examining the corruption – economic correlation are suggested as follows:

- First regression function: Examining the effect of corruption(CPI) on economic growth (GDP ) directly:

GDP it = α o + α 1 CPI it + α 2 I it + α 3 HC it + α 4 PI it + α 5 GOV it + α 6 OPEN it + ε it (1)

- Second regression function:Investigating the impacts of corruption (CPI) on economic development (GDP)indirectly through five transmission channels:

I it = β o + β 1 LnGDP it + β 2 CPI it + β 3 HC it + β 4 OPEN it +β 5 DLI i + β 6 DLMI i + β 7 DUMI i + β 8 DHIO i

HC it = δ o + δ 1 LnGDP it + δ 2 CPI it + δ 3 PSE it + δ 4 LnPOP it + δ 5 URBAN it +δ 6 DLI i + δ 7 DLMI i + δ 8 DUMI i + δ 9 DHIO i + δ 10 DHINO i +à it (3)

PI it = i o + i 1 LnGDP it + i 2 CPI it + i 3 GOV it + i 4 LnPOP it + i 5 URBAN it + i 6 DLI i + i 7 DLMI i + i 8 DUMI i + i 9 DHIO i + i 10 DHINO i + π it (4)

GOV it = j o + j 1 LnGDP it + j 2 CPI it + j 3 HC it + j 4 URBAN it +j 5 DLI i + j 6 DLMI i + j 7 DUMI i + j 8 DHIO i + j 9 DHINO i +Ω it (5)

OPEN it = x o + x 1 LnGDP it + x 2 CPI it + x 3 GOV it + x 4 HC it +x 5 DLI i + x 6 DLMI i + x 7 DUMI i + x 8 DHIO i + x 9 DHINO i + £ it (6)

The explanatory variables and dependent variables are defined as follow:

 GDP: Growth rate of real GDP (annual percent growth) This indicator measured by the annual percentage growth rate of gross domestic product (GDP) based on constant local currency.

 CPI: Corruption Perception Index (a unit) This index ranges from zero to ten whereby lower point indicates higher level of corruption and vice versa.

Gross domestic fixed investment reflects the annual percent growth in capital investment directed towards infrastructure improvements, including land enhancements such as fencing, drainage systems, water delivery, and sewage treatment It also encompasses expenditures on machinery, equipment, and plant purchases.

LE KIM Page schools, offices, hospitals, private residential apartments, commercial buildings, and industrial factories (World Bank Indicator definitions)

 HC: (Human Capital) Gross enrolment ratio, secondary, both sexes (percent).

It means the total enrollments for secondary education, regardless of age In the other words, this figure expressed as a percentage of the official secondary education age population.

 PI: Political stability and absence of violence (a unit) This indicator ranges from approximately -2.5 to 2.5 (weak to strong) governance performance. Higher PI index reflects lower instability in political situation.

General government final consumption expenditure, expressed as a percentage of GDP, encompasses all current government spending on goods and services, including employee benefits This measure also accounts for most expenditures related to national defense and security, while excluding military spending classified under national capital formation, according to World Bank definitions.

 OPEN: Openness to trade of GDP (percent of GDP) This indicator equals the ratio of sum of exports and imports of commodities and services to GDP.

 LnGDP: Logarithm of GDP (current USD)

 PSE: Government expenditure on education, total (percent of GDP)

 URBAN: Urban popupation (percent of total population)

 DLI (dummy variable) = 1 for low income countries.

 DLMI (dummy variable) = 1 for lower middle income countries.

 DUMI (dummy variable) = 1 for upper middle income countries.

 DHIO (dummy variable) = 1 for high income countries in OECD group.

 DHINO (dummy variable) = 1 for high income countries not in OECD group

This study examines the impact of corruption on various transmission channels using equations (2) to (6) By integrating these findings with equation (1), it calculates the indirect effect of corruption on economic growth through each channel, represented by the formula: (coefficient of corruption in equations 2 to 6) x α, where α ranges from α2 to α6 in equation (1).

Table 2: Model specification for Growth and Transmission channel equations:

Dependent variables GDP I HC PI GOV OPEN

Various methodologies have been utilized to examine the link between corruption and economic growth, including the generalized method of moments applied in cross-country analyses by researchers such as Campos et al (1999), Aidt (2009), and Mauro Paulo.

(1995, 1996), Pak Hung Mo (2001), Pellegrini and Gerlagh (2004); Threshold

The article discusses various economic models related to corruption and its impact on economic development, including the LE KIM Page model (Aidt et al., 2008; Haque and Kneller, 2009), dynamic general equilibrium models (Blackburn et al., 2006, 2008; Blackburn and Forgues-Puccio, 2009), a Stackelberg game model (Kaufmann and Wei, 2009), and an equilibrium queuing model (Lui, 1985) Additionally, it highlights the use of channel methodology to analyze how different transmission channels contribute to the relationship between corruption and economic growth (Mauro, 1995; Tavares and Wacziarg).

Empirical studies have indicated that many variables are endogenous, necessitating the use of the two-stage least squares (2SLS) method with instrumental variables to address bias from inconsistent coefficient estimates Unlike ordinary least squares (OLS), which cannot effectively handle endogenous variables, 2SLS provides a solution by ensuring no correlation between the instrumental regressor and the error term, a crucial condition for its application.

This study employs descriptive statistics, correlation matrices, scatter diagrams, and graphs to explore the relationship between corruption and economic growth while providing preliminary regression predictions It further investigates the indirect effects of corruption on growth through various transmission channels, including investment, human capital, political instability, government expenditure, and trade openness, utilizing the Channel methodology Additionally, the research analyzes the corruption-growth relationship in thirty selected countries using multiple techniques, including three-stage least squares (3SLS), pooled ordinary least squares (OLS), fixed effects method (FEM), and random effects method (REM).

The two-stage least squares (2SLS) method is the most widely used estimation technique for simultaneous equations models, employing an equation-by-equation approach In this method, endogenous regressors are instrumented with regressors from other equations Developed by Theil in 1953 and Busmann in 1957, 2SLS serves as a foundational tool in econometrics The three-stage least squares (3SLS) estimator, introduced by Zellner and Theil in 1962, builds upon this framework, offering enhanced estimation capabilities.

The LE KIM Page presents a unique scenario in multi-equation Generalized Method of Moments (GMM), where a common set of instrumental variables is utilized across all equations When all regressors are predetermined, the three-stage least squares (3SLS) method simplifies to seemingly unrelated regressions (SUR), effectively integrating two-stage least squares (2SLS) with SUR The 3SLS estimator capitalizes on more comprehensive information by analyzing all equations simultaneously, leading to more precise parameter estimates compared to 2SLS Therefore, this paper employs 3SLS regression for its analysis.

Diagnostic Checking for the equations:

Regression diagnostics are used to check on how well our data meet the assumption of pooled OLS regression There are some diagnostic checks below:

 Normality of residuals: Shapiro-Wilk W test

 Homoskedasticity of residuals (the error variance should be constant): White’s test, Breusch-Pagan test

 Multicollinearity (two variables are near perfect linear combinations of one another): VIF test

 Model specification: Ramsey Reset test

Data

This research utilizes panel data from thirty Asian countries, including Armenia, Azerbaijan, Bangladesh, and others, to achieve its objectives The advantages of using panel data are twofold: it enables control over unobservable factors and elements that change over time but not across entities, and it provides more informative and efficient results with reduced collinearity among variables, enhancing the degree of freedom by integrating time series and cross-sectional data.

The data is described briefly as follows:

- The observation period is from 1996 to 2014.

This article examines the interplay between key economic variables, including GDP, CPI, and I, highlighting their impact on economic growth and development It emphasizes the significance of Human Capital as a crucial component of the labor force and its relationship with political stability (PI) and public spending (GOV) The analysis also explores the role of trade openness (OPEN) in fostering economic resilience, alongside the importance of educational investment (PSE) for sustainable growth Additionally, demographic factors such as population (POP) and urbanization (URBAN) are discussed, alongside five dummy variables (DLI, DLMI, DUMI, DHIO, DHINO) that provide further insights into the dynamics of the economy.

The corruption data, represented by the Corruption Perception Index (CPI), is sourced from Transparency International (TI) Additional secondary data on various indicators is obtained from the World Bank's World Development Indicators and Global Development Finance database Specifically, the Political Instability Index (PI) is derived from the Worldwide Governance Indicators (WGI) project, developed by Daniel Kaufmann, Aart Kraay, and Massimo Mastrorillo in 2010.

The summary of the variables, Abbreviation of Data and Source of Data are briefly reported in Table 3:

Abbreviation Variable Unit Data sources

Growth GDP Growth rate of real

Corruption Perception Index (higher index, lower corruption level)

Investment I Gross domestic fixed investment

Human Capital HC Total enrollment in secondary education

Percent of population of secondary

LE KIM Page edu.age

Political stability and absence of violence (higher index, lower instability)

World Bank: Worldwide Governance Indicators Project

General government final consumption expenditure

Percent of GDP World Bank

Openness OPEN Openness to trade of

GDP Percent of GDP World Bank

Government expenditure on education, total

Percent of GDP World Bank

Population POP Total population in a country Person World Bank

Urban population in a country Percent of POP World Bank

DLI 1 for low income country

DLMI 1 for lower middle income country

DUMI 1 for upper middle income country

DHIO 1 for high income country in OECD group

DHINO 1 for high income country not in OECD group

Country sample (N0): Asian Countries observed period is from 1996 to 2014

ESTIMATION RESULT ANALYSIS AND DISCUSSION

Descriptive statistics analysis on the dataset

The descriptive statistics and correlations for selected elements are shown as Table

3 below The main results for the panel dataset are:

The average GDP growth rate in Asian countries from 1996 to 2014 is 5.345%, exhibiting significant variation with a low of -16.7% and a high of 34.5% This fluctuation can be attributed to numerous financial crises and economic downturns during that period Additionally, the average Corruption Perception Index (CPI) stands at 3.71, ranging from a minimum of 1, indicating a high level of corruption, to a maximum of 9.4, reflecting low corruption levels.

The correlation table reveals that investment, government expenditure, and trade openness (I, GOV, OPEN) are positively associated with economic growth, whereas corruption, political instability, and human capital (CPI, PI, HC) show a negative correlation with economic growth Notably, the correlations among CPI and PI (0.5933), CPI and HC (0.5636), and HC and PI (0.5499) indicate a strong relationship among these negative factors, suggesting they tend to move in the same direction Conversely, the correlations between CPI, PI, HC and I, GOV, OPEN are relatively low and acceptable Additionally, the mean Variance Inflation Factor (VIF) is 1.49, indicating a low level of multicollinearity among the variables.

LE KIM Page for equation 1 is reasonable to conduct the regressions with all the independent variables.

- The column Observation indicates the number of observations for each component The variables are available from 435 to 570 observations.

Variable name Obs Mean Std Dev Min Max

Annual percentage growth rate of 558 5.345 4.310 -16.7 34.5 real GDP

Annual percentage growth rate of 526 6.885 16.785 -58.786 111.4 gross domestic fixed investment

Ratio of total enrollment in secondary education to the 391 73.213 22.398 16.546 106.030 population of official secondary education age

Political stability and absence of 477 -0.481 0.928 -2.812 1.400 violence

Ratio of general government final 541 15.203 15.416 3.460 156.532 consumption expenditure to GDP

Ratio of exports and imports to 554 93.093 66.643 0.309 439.657

CPI (index) Fitted values GDP (% Growth)

Table 5:Correlations of selected variables:

Correlate GDP CPI I HC PI GOV OPEN

This paper also use graphs to show the relationship between every pair of variables clearly, beginning with economic growth and corruption:

Figure 2: Scatter graph between Economic growth (GDP) and Corruption (CPI)

The graph indicates a slight downward trend, with the Corruption Perception Index (CPI) serving as a proxy for corruption levels; a higher CPI reflects lower corruption This trend suggests a positive relationship between corruption and economic growth, indicating that corruption may enhance economic growth The correlation coefficient for this relationship is -0.2480.

Now, come to the pair of economic growth and political instability:

INVESTMENT (%Growth) Fitted values GDP (% Growth)

Figure 3: Scatter graph between Economic growth(GDP) and Political Instability (PI)

This research utilizes Political Stability and Absence of Violence (PI) as a metric for assessing the political climate, indicating that a higher PI signifies greater stability Figure 3 illustrates the challenge in establishing a clear relationship between economic growth and political instability, as evidenced by a minimal correlation coefficient of -0.1085 This suggests that political instability exerts a very weak positive influence on economic development.

The next consideration is the relationship between growth and investment

Figure 4: Scatter graph between Economic growth(GDP) and Investment (I)

Figure 4 clearly illustrates a strong positive linear relationship between economic growth and investment, as evidenced by an upward slope The correlation coefficient of 0.5143 indicates that increased investment significantly contributes to economic growth.

The forth pair is growth and government expenditure:

OPEN (Trade % of GDP) Fitted values GDP (% Growth)

GOV (% of GDP) Fitted values GDP (% Growth)

Figure 5: Scatter graph between Growth (GDP) and Human Capital (HC)

Human Capital has a minimal negative impact on economic growth, with a correlation coefficient of -0.1051 This paper will further explore this relationship using regression analysis in the following section.

Now, the relationship among economic growth, government expenditure, and openness to trade will be considered.

Figure 6: Scatter graph between Growth (GDP) and Openness to Trade (OPEN)

Figure 7: Scatter graph between Growth (GDP) and Government Expenditure (GOV)

Figures 6 and 7 illustrate a linear relationship between economic growth, trade openness, and government expenditure Specifically, the correlation coefficients are 0.0585 and 0.0728, indicating a positive relationship between these variables However, due to the relatively small size of these coefficients, this paper will revisit the analysis in the following section.

Besides the relationship between economic growth and its important determinants, the relationship between corruption and transmission channels are also considered.

Figure 8: Scatter graph between Corruption (CPI) and Investment (I)

The scatter diagram illustrates a downward sloping trend line, indicating a correlation of -0.1421 between the Consumer Price Index (CPI) and investment levels This suggests that a lower CPI is associated with higher investment However, a lower CPI also correlates with an increased level of corruption, implying that corruption may, paradoxically, stimulate economic growth.

Figure 9: Scatter graph between Corruption (CPI) and Human Capital (HC)

Figure 10: Scatter graph between Corruption (CPI) and Political Instability (PI)

Figure 11:Scatter graph between Corruption (CPI) and Government Expenditure (GOV)

Figure 12:Scatter graph between Corruption (CPI) and Trade Openness (OPEN)

Figures 9 to 12 illustrate a linear relationship between corruption and key factors such as human capital, political instability, government expenditure, and trade openness The correlation coefficients for these pairs are 0.5636, 0.5933, 0.1827, and 0.0877, respectively, indicating that increased corruption negatively impacts human capital, political stability, government spending, and trade openness.

Regression results and discussion

We first present the estimates of the single equation, and then the system of equations (1) to (6).

4.2.1 Effects of corruption on economic growth directly

Table 6: Results of Pooled OLS, Fixed Effect Model (FEM), and Random Effect Model (REM) in GDP regression model (Model 1)

Variables Pooled OLS FEM REM

(percent of total population of

Note: * Significant at 10%, ** Significant at 5%, *** Significant at 1%

Dependent variable is the annual percentage growth rate of real GDP(GDP) Results of Hausman test

To decide or evaluate the statistically significant correlation between fixed and random effects, we can run a Hausman test, in which:

-Null hypothesis (Ho): the estimation of FEM and REM are the same If (Prob>chi2) < 0.05, we reject Ho, FEM are better, and contrast.

In this case, P-value of Chi-square in Hausman test larger than 0.05 so it is safe or suitable to use random effects for regression model.

The REM regression results indicate a statistically significant negative coefficient for the corruption perception index (CPI), suggesting that a lower CPI, which reflects a higher level of corruption, positively impacts GDP growth Specifically, the coefficient α1 = -0.476 indicates that a one-unit decrease in CPI, representing an increase in corruption, correlates with a 0.476 percent rise in the GDP growth rate.

Test for normality of residuals - Results of Shapiro-Wilk W test:

The null hypothesis (Ho) posits that the residuals are normally distributed If the probability (Prob>z) is less than 0.05, we reject the null hypothesis and proceed with further analysis In this scenario, the P-value is extremely low (0.0000), leading us to conclude that we reject the assumption of normality for the residuals.

Test for homoscedasticity (heteroscedasticity) – Results of White’s test and Breusch Pagan test:

Null hypothesis (Ho): the variance of the residuals is homogenous If p-value

In our analysis, we found that the p-value for White's test is 0.0011 and for the Breusch-Pagan test, it is 0.0304 Since both values are below the significance level of 0.05, we reject the null hypothesis and accept the alternative hypothesis This indicates that the variance in our model is not homogenous.

Test for model specification – Results of Ramsey Reset test

Null hypothesis (Ho): the model specification is correctly formed

The alternative hypothesis (H1) suggests that the model specification is incorrectly formulated, which can happen if relevant variables are left out of the equation or if irrelevant variables are included.

If (Prob>F) < 0.05, we reject Ho, and contrast In this case, (Prob>F) is very small (0.0000), indicating that we reject that the model specification is correct.

In summary, econometric tests such as the Swilk-test, White's test, Breusch-Pagan test, and Ramsey-reset test have been utilized to demonstrate that Model 1 encounters issues like abnormal error term distribution, heteroscedasticity, and model specification errors When the assumptions of Ordinary Least Squares (OLS) regression are violated, it is crucial to avoid directly applying OLS to the structural equations of a simultaneous system.

4.2.2 Effects of corruption on economic growth through transmition channels

In Chapter 3, we applied Rodney Smith's (2010) method to calculate capital stock series from gross fixed capital formation However, using this as a proxy for investment in our system of equations yielded unsatisfactory results, leading to the omission of model 2 (refer to Appendix C.4) Consequently, this paper adopts Gross Domestic Fixed Investment as the primary measure of investment levels.

Table 7: Results of 3SLS regression in a system of structural equations (Equation 1 to 6)

Note: Obs 185, * Significant at 10%, ** Significant at 5%, *** Significant at 1%

In which: Endogenous variables: GDP, PI, I, GOV, OPEN, HC

Exogenous variables: CPI, LnGDP, LnPOP, URBAN, DLI, DLMI, DUMI, DHIO, DHINO

Table 8: Consequences of Corruption on Economic Growth through transmission channel

Effect of channel on Growth (α)

Effect of Corruption on channel

Effect of Corruption on Growth via channel (α * third column)

Table 8 highlights the relationship between corruption and economic growth in Asian countries, emphasizing various intermediary factors The analysis shows that investment, human capital, political instability, government spending, and trade openness have effects of 0.217, 0.013, 0.361, -0.098, and 0.012, respectively When examining corruption as an independent variable, its impact on these channels is significant, with values of 3.785 for investment, -3.510 for human capital, 0.291 for political instability, 3.013 for government spending, and 13.813 for trade openness The coefficients in the final column illustrate how corruption influences economic growth through these intermediary channels, necessitating further exploration of these results.

To prevent misunderstandings, LE KIM Page takes a conservative approach, highlighting that a positive coefficient signifies an adverse relationship between corruption and economic growth This is evidenced by the fact that a higher Corruption Perception Index (CPI) indicates a lower level of corruption, ultimately suggesting that reducing corruption can foster growth.

In other words, an increase in CPI is associated with higher economic growth, and contrast.

The findings indicate a positive relationship between investment and economic growth, with a 1% increase in gross fixed capital resulting in a 0.217% rise in GDP, aligning with the Solow growth model Although the coefficient for corruption's impact on investment is positive at 3.785, it lacks statistical significance, likely due to economic downturns from 1998 to 2000 and 2007 to 2009 Nevertheless, lower corruption levels correlate with increased capital investment, suggesting that a 1-unit rise in the corruption perception index could lead to an approximate 0.821 percentage point boost in economic growth through investment This negative correlation between corruption and growth is supported by previous research, including studies by Mauro (1995, 1996), Pak Hung Mo (2001), Pellegrini and Gerlagh (2004), and Mohamed Dribi (2013), which highlight that corruption diminishes entrepreneurs' investment incentives by raising transaction costs and reducing profits.

The relationship between human capital, measured by the percentage of total enrollment in secondary education relative to the official secondary education age population, shows a positive correlation with economic growth However, the coefficient is not statistically significant at the 10% level, preventing us from definitively concluding that human capital plays a crucial role in growth Nevertheless, the findings indicate that a 1% increase in secondary school enrollment corresponds to a 0.013% rise in economic growth This weak and ambiguous effect of human capital on growth aligns with the results from previous research conducted by Spiegel.

Research by Le Kim Page and Benhabib (1994) and Knowles and Owen (1995) indicates a negative and significant correlation between corruption and human capital, with a coefficient of -3.510 at a 1% significance level This suggests that corruption can inadvertently enhance human capital, as students may seek education despite corrupt practices to achieve better grades Consequently, the corruption index (CPI) negatively impacts economic growth (GDP) through human capital, with a coefficient of -0.046 This means that for every 10-point decrease in the corruption index, average economic development could decline by 0.46 percentage points annually, assuming other factors remain constant Interestingly, this finding contrasts with the research of Mo (2001), Pellegrini et al (2004), Ugur and Dasgupta (2011), and M Dribi (2013), highlighting the complex relationship between corruption and economic growth.

The 3SLS regression analysis reveals that the corruption index (CPI) significantly influences GDP growth, demonstrating a positive relationship through political instability (PI) Specifically, a 1-point increase in the CPI, indicating lower corruption, correlates with a 0.105 percentage point rise in economic growth by enhancing political stability This finding aligns with previous research by Mohamed Dribi (2013) and Muscatelli et al (2000) Countries characterized by low corruption and high political stability create a favorable investment environment, fostering economic development.

The analysis reveals that the ratio of general government final consumption expenditure (GOV) to GDP is statistically insignificant, even at a 10% level, indicating no direct linkage between GOV and GDP The negative coefficient of GOV (-0.098) suggests that a reduction in government spending may correlate with increased economic growth; however, this effect is minimal and not statistically significant In contrast, the coefficient of the Consumer Price Index (CPI) to GOV is 3.013, allowing us to assess the impact of corruption on growth through government spending The findings indicate a significant coefficient of -0.295, at a 1% level, suggesting that a 1-unit decrease in the corruption index could lead to an increase in GDP growth rate.

The findings indicate that a 0.295 percentage point increase in government expenditure correlates with higher levels of corruption, which may, in turn, stimulate economic growth This outcome aligns with the research conducted by Mohamed Dribi in 2013.

In countries with high levels of corruption, bureaucrats are more likely to allocate government budgets and public natural resources to sectors where it is challenging to accurately measure value, progress, and effectiveness.

CONCLUSION, LIMITATION AND FUTURE RESEARCH

Conclusion

This study explores the relationship between corruption and economic growth, utilizing both direct and indirect methods through various transmission channels We employed ordinary least squares and structural equations within a simultaneous system approach to analyze the data.

This study aligns with previous empirical research, concluding that corruption has a significantly negative impact on economic growth in thirty Asian countries from 1996 to 2014 Our regression analysis supports these findings and leads to several key conclusions.

- It is more suitable to use structural equations of a simultaneous system approach than ordinary least square approach (single equation approach)

- Corruption has a positive impact on economic growth However this coefficient is not significant.

Corruption has a complex relationship with economic growth, where its total positive indirect impact through human capital and government expenditure is merely 0.341% In contrast, the negative indirect effects stemming from factors such as investment, trade openness, and political instability are significantly higher, amounting to 1.092%.

- Investment and government spending are the vitual channels through which corruption transmits its impact on economic growth.

Limitations and future research

There are several limitations associated with this paper that we should take into consideration.

This study examines how corruption affects economic growth, specifically through the lens of Gross Domestic Product (GDP) growth rates However, the method used for quantifying this relationship has been overly generalized and systematized, which complicates the interpretation of the results.

LE KIM Page examines how corruption affects different sectors of the economy, highlighting that the progress seen in areas like agriculture, industry, trade, and manufacturing does not align with overall economic growth This study does not focus on measuring the extent of corruption or its specific influence on growth rates across various economic sectors.

Secondly, this study includes a large number of different countries with different economic environment Therefore, the lessons for specific country from other countries is limited.

This study overlooks the effects of financial crises on corruption and economic growth, revealing significant differences in growth patterns and corruption levels before, during, and after economic downturns The analysis shows that the investment and corruption coefficient in Model 2 is insignificant, with erratic growth trends observed from 1996 to 2014.

This study primarily examines the one-way relationship between corruption and economic growth through intermediary factors, neglecting the reverse influence of economic growth on corruption Exploring the two-way causality between these variables could offer a more comprehensive analysis and valuable insights for future research.

To improve the accuracy and depth of research outcomes, it is essential to incorporate macroeconomic factors such as legal frameworks, regulations, and oversight Additionally, expanding the sample size and including diverse global regions and methodologies will significantly enhance the findings.

According to the above-mentioned limitation of this paper, further researches should analyze the performance of different sectors including agriculture,

LE KIM Page manufacturing, industry, and trading, etc and how corruption could make influence on each aspect at an intensive level.

Future research should focus on determining the optimal level of corruption, identifying the threshold where economic growth shifts from positive to negative Policymakers need to establish anti-corruption standards that mandate prompt action when corruption surpasses this critical level.

Future researchers should examine the reciprocal relationship between corruption and economic growth, as understanding this dynamic is essential for elucidating the correlation between corruption and economic development Additionally, it is crucial to consider the potential duplication effect of these variables, given that their impact can be transmitted through various channels.

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APPENDIX A : Summary of empirical studies

No Authors Methodology Data Results

Meta-analysis 460 empirical estimates about the the relationship between corruption and economic growth from

41 different theoretical and empirical investigations.

32% of the estimates stated a significant and negative effect of corruption on growth, 62% showed a insignificant correlation, only 6% supported for a significant and positive relation.

Meta-analysis 115 analytical studies focus on low income countries to evaluate the effect of corruption on economic growth

The overall effect (direct and indirect) of corruption on GDP growth per capita in LICs (Low Income Countries) is -0.59 percentage point.

Corruption would tend to impact growth negatively via government expenditure (-0.23 percentage point) and human capital (-0.29 percentage point) In mixed countries, corruption have

“greasing the wheels”effect through investment channel (only +0.04 percentage point)

Panel dataset collected by Robert Barro and

Corruption has a negative impact on growth The

Jong-Wha Lee's analysis from 1960 to 1985 reveals a total effect of -0.545 percentage points, with specific contributions from various factors: the direct effect accounts for -11.8 percentage points, human capital contributes -14.8 percentage points, political instability has a significant impact of -52 percentage points, and investment influences the total by -21.4 percentage points.

3700 firms from 69 developed and developing countries

Negative impacts of unpredictable corruption on economic growth via investment channel.

Data set covering 70 developed and developing countries.

Observation period is from 1980 to 1983

Corruption impacts on economic growth and investment signigicantly and negatively.

Data of 94 countries during the sample period 1960-1985

Corruption reduces private investment and declines public spending for education Hence, corruption has a negative effect on economic development.

General Negative relationship between corruption and growth, expressed in detail,

The LE KIM Page methodology reveals that a one-unit increase in the corruption perception index leads to a 0.38 percentage point decline in per capita output growth Corruption primarily impacts growth through its negative effect on investment, with fixed investment contributing 32%, trade openness 28%, political instability 16%, and human capital 5% to the overall detrimental effect.

The relationship between corruption and economic growth is non-linear, heavily influenced by the quality of governance In nations with strong institutional frameworks, corruption has a pronounced and detrimental effect on growth Conversely, in countries characterized by weak governance, corruption appears to have little to no impact on economic performance.

Data covering 82 both developed and developing over the

Negative impacts of corruption on growth The most important

From 1980 to 2002, corruption significantly impacted economic growth through various channels, notably human capital and political instability, which contributed negatively at rates of -0.5% and -0.46%, respectively The overall negative effects of corruption on growth were attributed to human capital (36.6%), investment (22.9%), inflation (6.6%), and political instability (33.8%) Interestingly, corruption also exhibited a positive effect on growth through increased government expenditure.

Data covering 54 developed and developing countries with observed period 1980-2003

Corruption has a significant negative impact on economic growth, with this relationship influenced by cultural factors Changes in culture can weaken the threshold of this relationship, leading to further detrimental effects Additionally, since most resources are state-owned, government priorities are primarily focused on managing these assets effectively.

LE KIM Page corruption, so development traps move up

African countries with observed period from

Corruption has a strong negative impact on domestic investment and economic growth.

General Corruption as “speed money” is efficient to economic growth.

General “Grease the wheel” thesis, positive relationship between corruption and growth.

14 Lien (1986) General Competitive bribery game with incomplete information

Corruption may be helpful to economic development

Build up the model based on Stackellberg game

Global 3866 firms survey from 48 countries in 1996 and 1997 no evidences support for the “speed money” thesis,expressed differently, they refuted “efficient grease” thesis, so corruption reduces economic growth

C.1 Declare data to the panel

xtset id year panel variable: id (strongly balanced) time variable: year, 1996 to 2014 delta: 1 unit

C.2 Calculating capital from the gross domestic fixed investment

by id: egen avg_ggfK=mean( igrowth )

replace avg_ggfK=avg_ggfK/100

by id: gen K= iusd[1]/(depreciation+avg_ggfK)

by id: replace K=(1-depreciation)* L.K+ iusd if _n>1

graph twoway (lfit variable1 variable2) (scatter variable1 variable2)

sum gdpgrowth cpiunit igrowth hc piunit govgdp opengdp

correlate gdpgrowth cpiunit igrowth hc piunit govgdp opengdp

Collin cpiunit igrowth hc piunit govgdp opengdp

LE KIM Page cpiunit 1.87 1.37 0.5358 0.4642 igrowth 1.21 1.10 0.8233 0.1767 hc 1.64 1.28 0.6084 0.3916 piunit 1.82 1.35 0.5487 0.4513 govgdp 1.27 1.13 0.7887 0.2113 opengdp 1.11 1.05 0.8993 0.1007

collin cpiunit igrowth hc piunit govgdp opengdp

Variable VIF VIF Tolerance Squared

Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept)

Pooled OLS regression results of GDP model (Model 1)

reg gdpgrowth cpiunit igrowth hc piunit govgdp opengdp

Source SS df MS Number of obs = 253

Adj R-squared = 0.2883 Root MSE = 3.8442 gdpgrowth Coef Std Err t P>|t| [95% Conf Interval] cpiunit -.5154352 2099528 -2.46 0.015 -.9289696 -.1019008 igrowth 1652213 0185209 8.92 0.000 1287415 2017011 hc 0129003 0153824 0.84 0.402 -.0173978 0431984 piunit -.1179124 3505925 -0.34 0.737 -.8084585 5726337 govgdp -.0358273 0226089 -1.58 0.114 -.0803591 0087044 opengdp 004797 005932 0.81 0.419 -.0068871 016481

FEM regression results of GDP model (Model 1)

xtreg gdpgrowth cpiunit igrowth hc piunit govgdp opengdp,fe

Fixed-effects (within) regression Number of obs = 253

Group variable: id Number of groups = 26

F(6,221) = 12.68 corr(u_i, Xb) = -0.1936 Prob > F = 0.0000 gdpgrowth Coef Std Err t P>|t| [95% Conf

Interval] cpiunit 3331118 603185 0.55 0.581 -.8556188 1.521842 igrowth 1561048 0186316 8.38 0.000 1193865 1928231 hc -.0219766 0405505 -0.54 0.588 -.1018917 0579385 piunit 1347567 7562715 0.18 0.859 -1.35567 1.625183 govgdp -.1257954 1001291 -1.26 0.210 -.3231256 0715347 opengdp 024225 0187165 1.29 0.197 -.0126607 0611108

_cons 4.599427 3.710579 1.24 0.216 -2.71322 11.91207 sigma_u 2.6531922 sigma_e 3.6222947 rho 34917027 (fraction of variance due to u_i)

REM regression results of GDP model (Model 1)

xtreg gdpgrowth cpiunit igrowth hc piunit govgdp opengdp,re

Random-effects GLS regression Number of obs = 253

Group variable: id Number of groups = 26

R-sq: within = 0.2431 Obs per group: min = 1 between = 0.5533 avg = 9.7 overall = 0.3046 max = 15

Wald chi2(6) = 95.31 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 gdpgrowth Coef Std Err z P>|z| [95% Conf

Interval] cpiunit -.4756252 2696616 -1.76 0.078 -1.004152 0529017 igrowth 1599184 0180012 8.88 0.000 1246367 1952002 hc 0088387 0192831 0.46 0.647 -.0289555 0466329 piunit -.1357008 4215096 -0.32 0.747 -.9618445 6904429 govgdp -.0378981 0260947 -1.45 0.146 -.0890428 0132465 opengdp 0070921 0080145 0.88 0.376 -.0086161 0228003

1.164715 3.6222947 09370082 (fraction of variance due to u_i)

Hausman test for FEM and REM of GDP model (Model 1)

(b-B) Difference sqrt(diag(V_b-V_B)) S.E. cpiunit 3331118 -.4756252 808737 5395505 igrowth 1561048 1599184 -.0038136 0048054 hc -.0219766 0088387 -.0308153 0356722 piunit 1347567 -.1357008 2704574 6279141 govgdp -.1257954 -.0378981 -.0878973 0966691 opengdp 024225 0070921 0171329 0169138 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Shapiro – Wilk W test for normality of residual in OLS regression (Model 1)

Shapiro-Wilk W test for normal data

Homoscedasticity of residuals test (Model 1)

Cameron & Trivedi's decomposition of IM-test

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance

Variables: fitted values of gdpgrowth chi2(1) = 4.69

Homoscedasticity of residuals test (Model 1)- Ramsey Reset test

Source SS df MS Number of obs = 253

Adj R-squared = 0.3354 Root MSE = 3.7149 gdpgrowth Coef Std Err t P>|t| [95% Conf

Ramsey RESET test using powers of the fitted values of gdpgrowth Ho: model has no omitted variables

3SLS regression for several equations

*Reg3 command with Gross domestic fixed investment (I) – proxy for capital

The analysis of economic indicators reveals significant relationships among GDP growth, consumer price index (CPI), government expenditure, and urban population dynamics Key metrics such as long-term GDP in USD, population growth, and health care expenditures illustrate the interconnectedness of these factors Furthermore, the examination of growth rates alongside CPI and government GDP highlights the influence of public spending on economic performance Understanding these correlations is essential for developing effective economic policies that promote sustainable growth and improve living standards.

Three-stage least-squares regression

Equation Obs Parms RMSE "R-sq" chi2 P gdpgrowth 185 6 4.006249 0.2516 26.47 0.0002 piunit 185 9 7128735 0.4546 158.65 0.0000 igrowth 185 9 13.72176 0.0995 64.87 0.0000 govgdp 185 9 11.00549 0.2965 408.54 0.0000 opengdp 185 9 41.12856 0.0833 1336.21 0.0000 hc 185 10 12.30538 0.6324 8050.52 0.0000

Coef Std Err z P>|z| [95% Conf Interval] gdpgrowth cpiunit piunit igrowth govgdp opengdp hc

353146 2.935143 438271 0277591 0379714 0679632 13.10168 piunit cpiunit govgdp lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino

4401425 0205526 3500773 0636506 -.0029047 -.6375903 -.2852201 0538677 000472 2.649453 igrowth cpiunit hc lngdpusd opengdp dli dlmi dumi dhio dhino

8.626659 3302571 -.7452203 0979504 164.7687 183.1468 187.0332 168.4226 159.886 govgdp cpiunit hc lngdpusd urbanpop dli dlmi dumi dhio dhino

5.159503 3001624 -2.960954 -.0607962 133.5281 153.3533 156.7431 171.6786 152.6316 opengdp cpiunit lngdpusd hc govgdp dli dlmi dumi dhio dhino

LE KIM Page hc cpiunit -3.50982 1.321533 -2.66 0.008 -6.099978 -.9196627 lngdpusd 13.95158 2.020281 6.91 0.000 9.991906 17.91126 edugdp 1.551186 647293 2.40 0.017 2825151 2.819857 lnpop -17.7138 2.078573 -8.52 0.000 -21.78773 -13.63987 urbanpop -.1379824 0886948 -1.56 0.120 -.311821 0358562 dli 34.80135 18.30126 1.90 0.057 -1.068452 70.67116 dlmi 45.61234 19.63624 2.32 0.020 7.126013 84.09867 dumi 38.92841 21.32442 1.83 0.068 -2.866683 80.7235 dhio 51.56339 23.37033 2.21 0.027 5.75838 97.36841 dhino 34.59282 21.59988 1.60 0.109 -7.742162 76.92781

Endogenous variables: gdpgrowth piunit igrowth govgdp opengdp hc

Exogenous variables: cpiunit lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino edugdp

*Reg3 command with Capital stock series (K) calculated following method proposed by Rodney Smith (2010) – proxy for capital

The analysis of economic indicators such as GDP growth, consumer price index (CPI), and government expenditure reveals significant relationships among various factors Key metrics include per capita income, urban population dynamics, and health care expenditures, which collectively influence economic performance Additionally, the interplay between government GDP and open GDP highlights the importance of fiscal policies in shaping economic outcomes Understanding these relationships is crucial for policymakers aiming to enhance economic stability and growth.

Three-stage least-squares regression

Equation Obs Parms RMSE "R-sq" chi2 P gdpgrowth 163 5 6.793868 -0.9598 15.53 0.0083 piunit 163 9 5307033 0.6592 423.78 0.0000 k 163 0 7.53e+12 -0.1752 govgdp 163 8 11.29342 0.3207 73.67 0.0000 opengdp 163 8 48.94792 -0.2401 383.47 0.0000 hc 163 9 10.14695 0.6399 382.98 0.0000

Coef Std Err z P>|z| [95% Conf Interval] gdpgrowth cpiunit piunit k govgdp opengdp hc

-.2303119 6.272839 -1.05e-13 3026524 0422952 0642815 30.57343 piunit cpiunit govgdp lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino

4553923 0262272 1331635 1024583 0004436 -.9205418 -.181494 0514524 6148189 2.318664 k cpiunit hc lngdpusd opengdp dli dlmi dumi dhio dhino

(omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) govgdp cpiunit hc lngdpusd urbanpop dli dlmi dumi dhio dhino

5.148825 1147118 -3.579859 003655 8958745 16.18113 15.44364 30.32115 182.6229 opengdp cpiunit lngdpusd hc govgdp dli dlmi dumi dhio dhino

LE KIM Page hc cpiunit -1.735516 1.007481 -1.72 0.085 -3.710142 2391098 lngdpusd 12.03271 1.482803 8.11 0.000 9.126465 14.93895 edugdp -1.407223 5206183 -2.70 0.007 -2.427616 -.3868293 lnpop -16.09899 1.539084 -10.46 0.000 -19.11554 -13.08244 urbanpop -.2030719 0657741 -3.09 0.002 -.3319867 -.074157 dli -.0072011 6.949825 -0.00 0.999 -13.62861 13.6142 dlmi 13.50716 5.589678 2.42 0.016 2.551591 24.46272 dumi 6.983277 4.754113 1.47 0.142 -2.334613 16.30117 dhio 21.00772 4.669553 4.50 0.000 11.85556 30.15987 dhino 0 (omitted)

Endogenous variables: gdpgrowth piunit k govgdp opengdp hc

Exogenous variables: cpiunit lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino edugdp

reg3( gdpgrowth cpiunit piunit igrowth govgdp opengdp hc)( igrowth cpiunit hc opengdp lngdpusd)( hc cpiunit lngdpusd)( piunit cpiu

> nit govgdp lngdpusd)( govgdp cpiunit hc lngdpusd)( opengdp cpiunit hc govgdp lngdpusd) Equation is not identified does not meet order conditions

Equation gdpgrowth: gdpgrowth cpiunit piunit igrowth govgdp opengdp hc Exogenous variables: cpiunit lngdpusd r(481);

*Reg3 command with CPI in square format

reg3(gdpgrowth sqcpi piunit igrowth govgdp opengdp hc)( piunit sqcpi govgdp lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino)( igr

> owth sqcpi hc lngdpusd opengdp dli dlmi dumi dhio dhino)( govgdp sqcpi hc lngdpusd urbanpop dli dlmi dumi dhio dhino)( opengdp sqc

> pi lngdpusd hc govgdp dli dlmi dumi dhio dhino)( hc sqcpi lngdpusd edugdp lnpop urbanpop dli dlmi dumi dhio dhino)

Three-stage least-squares regression

Equation Obs Parms RMSE "R-sq" chi2 P gdpgrowth 213 6 4.539146 -0.0377 25.15 0.0003 piunit 213 10 7171053 0.4196 362.13 0.0000 igrowth 213 9 15.34428 0.1196 77.87 0.0000 govgdp 213 9 12.46649 0.0063 343.91 0.0000 opengdp 213 9 38.45849 0.1248 1287.23 0.0000 hc 213 9 13.9284 0.5866 382.05 0.0000

Coef Std Err z P>|z| [95% Conf Interval] gdpgrowth sqcpi piunit igrowth govgdp opengdp hc

0312363 1.688024 4045317 -.0099152 0356612 0970822 7.805273 piunit sqcpi govgdp lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino

035236 0166144 2223405 0771333 -.0027254 2.255138 3.038947 3.730426 4.273281 4.875879 igrowth sqcpi hc lngdpusd opengdp dli dlmi dumi dhio dhino

5572053 137195 005945 2542448 123.4555 138.7808 140.9274 138.1944 124.7118 govgdp sqcpi hc lngdpusd urbanpop dli dlmi dumi dhio dhino

4630984 6935059 -1.489589 -.0994977 89.10135 100.9696 103.8591 111.3919 103.2312 opengdp sqcpi lngdpusd hc govgdp dli dlmi dumi dhio dhino

1.617009 5277843 77676 3.022734 218.5248 216.5528 261.3201 187.4795 214.3863 hc sqcpi lngdpusd edugdp lnpop urbanpop dli dlmi dumi dhio dhino

Endogenous variables: gdpgrowth piunit igrowth govgdp opengdp hc

Exogenous variables: sqcpi lngdpusd lnpop urbanpop dli dlmi dumi dhio dhino edugdp