Corruption Levels and Country Cluster: A Comparative Analysis

Corruption Levels and Country Cluster: A Comparative Analysis

DOI: 10.4018/978-1-6684-8536-1.ch004
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The objective of the chapter is to classify countries into different categories of corruption levels using the K-Means algorithm for 2012 and 2021. The sample is made up of an aggregation of 195 countries of different levels of income using data obtained from the World Development Indicators compiled by the World Bank and Transparency International. The optimal number of clusters is chosen at the elbow point, which is the point of the graph where the curve starts to flatten out. The optimal number of clusters chosen is three, which can be qualitatively interpreted as groups of countries with high, medium, and low corruption levels. The results obtained show that corruption has a directly proportional relationship with the number of adolescent pregnancies, gender inequality, overall inequality, and maternal mortality. While corruption levels have a negative relationship with levels of human development and the average years of schooling.
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Feedback loops refer to a process in which the decisions or actions of individuals or organizations affect the conditions or environment, which in turn influences future decisions or actions (Rivera & Rivera, 2019). The relationship between feedback loops and corruption is particularly important to consider, as corrupt actions can create self-reinforcing cycles that amplify negative consequences. For example, if a government official accepts a bribe, this can lead to a misallocation of resources and further corruption, which can in turn undermine public trust in institutions and lead to decreased compliance with laws and regulations. This can create a cycle where corruption becomes increasingly normalized and widespread, ultimately causing significant harm to the economy and society as a whole. Understanding and addressing feedback loops is therefore critical to combating corruption and promoting transparency and accountability. The foregoing highlights the importance of studying corruption and its relations with the economy.

Within this diversity, most of the works can be classified into four groups: theoretical models, negative consequences of corruption, case studies and possible solutions. The first group includes the works of Aidt (2003) on analytic approaches for understanding the different ways in which corruption can arise and persist, Acemoglu & Verdier (2000) about microeconomic analysis of the functioning of the government and how its incentive structure can generate or stop corruption cases, Fisman (2001) on a framework for the prevalence of political rents and political connections, and Persson Tabellini & Trebbi (2003) about how the electoral system influences corruption can help design more effective policies to combat it.

While some relevant studies on the negative consequences of corruption are Chen, Ding & Kim (2010) discussing the impact of political connections on the earnings forecasts of financial analysts and highlights the challenges faced by multinational enterprises (MNEs) due to political forces, and Ajaz & Ahmad (2010) highlighting the institutional problems that developing countries face in generating revenue and examine the impact of corruption and governance on tax collection. In the third group of literature we can find the works of Paul (2010) about the relationship between corruption and economic growth in Bangladesh, Ferraz & Finan (2011) about how political institutions affect corruption levels in local governments in Brazil, and Brollo, Nannicini, Perotti & Tabellini (2013) explore the impact of additional government revenues on political corruption and the quality of politicians, using both theoretical models and empirical data from Brazil.

In the group of possible solutions and recommendations, some relevant research includes Johnston & Kpundeh (2005) discussing the effectiveness of social action coalitions in combating corruption in societies, Gorsira, Steg, Denkers & Huisman (2018) on how organizational and individual factors contribute to corruption and whether an organization's ethical climate affects corruption through individual motives for corruption, Shah (2006) on decentralized local governance, Kaufmann (2005) calling for a bolder approach to governance and anti-corruption policies, Bobonis, Fuertes & Schwabe (2016) on how monitoring corrupt activities can lead to a short-term reduction in corruption, and Jeppesen (2019) about the importance of auditing to contribute significantly to the fight against corruption.

Key Terms in this Chapter

Correlation Matrix: Table that shows the correlation coefficients between several variables. It is used to determine the strength and direction of the relationship between two or more variables.

Human Capital: Knowledge, skills, and abilities of individuals that contribute to economic development. It includes education, training, and experience, and is a key factor in determining the productivity and competitiveness of an economy.

Governance: Rules and principles by which the company is governed when developing its operations. This includes interaction with all its stakeholders such as customers, suppliers, competitors, among others.

Gender Inequality: Unequal treatment and opportunities between men and women. It is a pervasive problem that affects education, health, employment, and political representation. It results in women being disadvantaged in various aspects of their lives.

Cluster: Group of data points that share similar characteristics and are identified based on their proximity to each other. It is a technique used in data mining and machine learning to group similar data points together for further analysis.

Inequality: Unequal distribution of resources, opportunities, and benefits within a society. It is a major challenge for economic and social development, and can lead to social unrest, political instability, and reduced economic growth.

Economic Development: Sustained growth of a country's economy through the expansion of trade, investment, and innovation. It involves increasing the productivity and competitiveness of the economy, creating job opportunities, and improving the standard of living for the population.

Machine Learning: Branch of statistics that involves developing algorithms that can learn from data and make predictions or decisions. It is used in a wide range of applications, from image recognition and natural language processing to fraud detection and recommendation systems.

Corruption: Abuse of power for personal gain. It involves the misuse of public resources, bribery, nepotism, and other forms of dishonest behavior that undermine the rule of law, democratic institutions, and economic development.

Transparency: Openness and accessibility of information, decision-making, and governance processes. It is a key aspect of good governance, and helps to promote accountability, reduce corruption, and build trust between citizens and institutions.

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