Search Results (23)

This paper analyzes the impact of corruption on foreign direct investment (FDI) in the Portuguese-speaking African countries (PALOP) economies between 2006 and 2018. The focus lies on Angola, Cape Verde, Guinea-Bissau, and Mozambique since, according to Transparency International, they exhibit intermediate to low levels on the Corruption Perceptions Index. Despite sharing historical and cultural ties, as former Portuguese colonies, no research has focused on the impact of corruption on FDI in the PALOP economies, to the best of our knowledge. To accomplish this, we use an Instrumental Variables Fractional Probit Regression applied to data from the World Bank Enterprise Surveys, which gather information for 2180 firms. The results show that, on average, corruption does not significantly affect FDI in PALOP economies. Trade, credit, and firm size emerge as key FDI determinants, while investment levels and tax rates are not relevant. Corruption has negligible effects on FDI in manufacturing but boosts FDI in services. Interestingly, while corruption has no significant effect on FDI for small and medium firms, a positive, significant impact is revealed for large firms. Finally, corruption’s overall FDI impact is the same across PALOP countries, except in Angola, where it negatively influences FDI compared to Mozambique. Full article

This paper examines the relationship between corruption and foreign direct investment (FDI) inflows in European Union member states using a dynamic panel framework. Using an unbalanced EU panel from 2002 to 2022 and an Arellano–Bond difference-GMM specification, we model inward FDI inflows per capita as a function of institutional integrity (measured by Transparency International’s Corruption Perceptions Index), market size, development level, and trade integration. The results show a robust positive association between improvements in perceived integrity (higher CPI scores) and increases in inward FDI inflows per capita, conditional on macroeconomic controls and dynamic adjustment. Market size and trade variables have the expected signs, while GDP per capita is the empirically sensitive margin, consistent with the idea that higher development can indicate greater purchasing power but also higher costs and saturation effects in advanced economies. Robustness checks using the inverse hyperbolic sine transformation—suited to heavy tails, zeros, and negative net flows—confirm that the governance association is not an artifact of scaling. The findings highlight the importance of institutional quality and market openness as correlates of FDI attractiveness within the EU. Full article

This study examines how socialization costs, inspection costs, collection costs, motor vehicle tax rates (Pajak Kendaraan Bermotor, PKB), vehicle ownership transfer tax rates (Bea Balik Nama Kendaraan Bermotor, BBNKB), the Corruption Perception Index (CPI), and the Indonesian Digital Society Index (Indeks Masyarakat Digital Indonesia, IMDI) influence regional tax compliance across 34 provinces in Indonesia, using secondary data from 2020 to 2024. Guided by Fiscal Federalism, Tax Optimization Theory, and the Fischer Tax Compliance Model, the analysis integrates spatial regression and SWOT to capture both structural and spatial dynamics in provincial tax administration. The spatial error model reveals that socialization costs, PKB, and BBNKB significantly shape provincial tax compliance. At the same time, the other variables show no measurable effect. Spatial clustering indicates High–High compliance in Central Java, Low–Low compliance in South Sumatra and Lampung, and Low–High compliance in North Sumatra. The SWOT assessment places Indonesia’s provincial tax compliance strategy in Quadrant I, suggesting strong institutional capacity and substantial external opportunities to support aggressive improvement strategies. This study contributes by providing province-wide empirical evidence on the fiscal and administrative determinants of compliance and by incorporating collection costs and spatial relationships into the analysis. Policy implications include strengthening targeted socialization, improving rate-setting mechanisms, and expanding digital reporting systems to enhance taxpayer understanding and administrative transparency. Full article

This study aims to compare the accuracy performances of different machine learning algorithms (Logistic Regression, Decision Tree, Support Vector Machines (SVMs), Random Forest, Artificial Neural Network, and XGBoost) using World Happiness Index data. The study is based on the 2024 World Happiness Report data and employs indicators such as Ladder Score, GDP Per Capita, Social Support, Healthy Life Expectancy, Freedom to Determine Life Choices, Generosity, and Perception of Corruption. Initially, the K-Means clustering algorithm is applied to group countries into four main clusters representing distinct happiness levels based on their socioeconomic profiles. Subsequently, classification algorithms are used to predict the cluster membership and the accuracy scores obtained serve as an indirect measure of the clustering quality. As a result of the analysis, Logistic Regression, Decision Tree, SVM, and Neural Network achieve high accuracy rates of 86.2%, whereas XGBoost exhibits the lowest performance at 79.3%. Furthermore, the practical implications of these findings are significant, as they provide policymakers with actionable insights to develop targeted strategies for enhancing national happiness and improving socioeconomic well-being. In conclusion, this study offers valuable information for more effective classification and analysis of World Happiness Index data by comparing the performance of various machine learning algorithms. Full article

The primary aim of this research is to examine the impact of corruption on the attainment of the Sustainable Development Goals (SDGs) in different countries. To achieve this, the study utilizes the Corruption Perceptions Index (CPI), one of the most widely recognized indicators of corruption. Additionally, the SDG Index is used to evaluate each country’s overall progress toward the 17 SDGs, with scores ranging from 0, representing the worst possible outcome, to 100, indicating achievement of the targets. In this work, the Tucker method has been applied, which has not previously been used in studies on SDGs and corruption and thus provides some novelty to the present research. This method has allowed us to analyze the relationship between the CPI and SDGs. The results obtained show that the lower the level of corruption in a country, the better SDGs are achieved. Thus, it has been observed that CPI scores are closely related to the achievement of goals related to Gender Equality (SDG5), Peace, Justice, and Strong Institutions (SDG16), and Reduced Inequalities (SDG10). This means our findings are extremely useful for enabling governments and institutions to roll out more effective policies and encourage investment for achieving the SDGs related to their region and the pressing need to combat corruption. As a conclusion, this study demonstrates that lower levels of corruption, particularly in Europe and North America, are strongly associated with progress toward SDGs related to Peace, Justice, and Strong Institutions. In contrast, high levels of corruption in regions such as Sub-Saharan Africa and South Asia significantly hinder the achievement of key SDGs, particularly those concerning Decent Work and Economic Growth, as well as Climate Action. Full article

Corruption is a global problem that affects the fair distribution of wealth of every country to different degrees and represents a problem to be solved to prevent the diversion and waste of resources. Among the different efforts to first measure it and later reduce it by proposing strategies, there exist a variety of indices, such as the corruption perception index, and other related issues, such as the global impunity index, the laxness of anticorruption policies, etc., which are computed for different countries worldwide. Based on these indices, we propose a model for corruption using a system of ordinary differential equations, considering anticorruption policies. Those three factors were identified after analyzing the phenomenon and available data, particularly for Mexico. Also, we fit it to the reported data of this country and perform simulations expecting to predict the short term, and performed a sensitivity analysis. The model is capable of reproducing the observed oscillatory behavior of the phenomenon. The model fit can still be improved by including the data for the anticorruption policies, which were only studied for different scenarios. Moreover, the model is susceptible to application in other countries, as long as data are available, and then provides a computational tool to predict and visualize the effect of appropriate public policies to fight corruption. Full article

This study examines the relationship between country-level corruption (proxied by the Corruption Perception Index, CPI) and firm performance (measured by Return on Assets, ROA) across 18,286 firms in the East Asia, South Asia, and Southeast Asia regions. Additionally, the moderating effects of audit quality (proxied by auditors’ reputation) on the relationship are examined. The findings of the study indicate a positive association between corruption and ROA in high-income nations, thus providing evidence in favor of the “greasing the wheel” theory. On the other hand, a negative association is documented in the upper middle- and low-income nations, which is consistent with the “sanding the wheel” notion. Notably, audit quality has a positive moderating influence on the relationship between corruption and ROA, especially in nations with low corruption levels, reaffirming the pivotal role of reputable auditors in enhancing firm performance within these economic contexts. The results of this study have important ramifications for forming policy suggestions and enhancing governance. The findings highlight the opportunity to improve governance practices and regulations to reduce corruption and increase transparency. Policymakers can develop ways to strengthen institutional frameworks by recognizing the complex link between corruption, corporate profitability, and the function of respected auditors. Full article

This study aims to carry out a comparative analysis between the level of digitization of the Romanian public administration compared to that existing in the most digitally developed states at the European level. Our study identifies the extent to which Romanian citizens have access to non-bureaucratic and transparent public services that support social inclusion and non-discrimination, compared to European citizens from states with the best digitalization of public services. Also, our research studies the relationship between the level of digitalization quantified by the DESI indicator and the level of income for the states considered in the analysis, as well as the relationship between digitalization and bureaucracy, the corruption index, and the digital skills of citizens. Based on the 486 statistical data collected and centralized on the corruption index (CPI), as well as the values for DESI and GNI per capita, for the period 2017–2022 for the 27 EU member states, we performed a statistical analysis using SPSS 28 regarding the existence of a DESI relationship and level of income (GNI per capita) and/or CPI (Corruption Perceptions Index). Our study is on a current issue, as it addresses the issue of digitalization of public administration, in the new post-pandemic and geostrategic context. It has theoretical applicability, by determining a model that can be used to study the relationship between digitalization and the standard of living and corruption, and also practical application, because it can contribute to the awareness of the government in taking measures and adopting strategies to reduce gaps as compared to the most developed digital states. Full article

By considering a panel dataset between 1995 and 2019 including several countries in South America and methodologically using the fixed effect and GMM methods in first differences, the authors sought to empirically determine the relationship between traditional energy consumption, renewable energy consumption, and economic growth. The results show that the two main variables studied (fossil energy consumption and renewable energy consumption) are statistically significant and contribute to economic growth per capita in all nine South American countries studied. Furthermore, it should be noted that this significance persists in the four models discussed in this study, demonstrating a link between the positive economic impact of reducing traditional energy consumption and increasing renewable energy consumption in the South American countries studied. This article also contributes to the existing literature by highlighting the fundamental role of gross capital formation, labor force participation, and tertiary school enrollment in the economic growth of these countries. Two rather small effects on the aforementioned growth are the corruption perception index and domestic lending to the private sector by banks. This paper calls on policymakers to reconsider increasing energy production using renewable sources and to promote measures for its consumption. Full article

This study investigates the privatization situation in Indonesia between 1996 and 2020. This study conducts a comparative analysis to see the impact of privatization on companies’ performance and, with regard to the crisis that occurred with respect to the companies that have been privatized, its economic impact. Furthermore, the determinants of the government’s decision to release its share from state-owned companies are also identified using the regression method. This study found, with regard to the state-owned enterprises in Indonesia, the impact of the economy as there were no differences in the companies’ performance before and during the crisis. This study found that the ability of the company to generate profits declined after privatization, but the company’s efficiency improved. Otherwise, the debt ratio of state-owned companies decreased after the privatization was carried out. Almost the same results were found when comparing the long-term performance with the short-term performance of the privatized SOEs. The determining factors that influenced the Indonesian government’s decision to divest its shares in state-owned companies were Indonesia’s corruption perception index ranking, the company’s ability to generate profits on its sales, and government ownership percentage stock in SOEs. Full article

The influence of globalization processes, the customs space of the country, requires the development and implementation of a transparent state customs policy to ensure security and integration into the space of the higher hierarchical order. The purpose of the study is to form scientific-applied recommendations regarding the development vectors of the customs space of a country in the global environment to improve its risk management system. The main method of study is econometric modeling, namely, canonical analysis in determining the interdependence of sustainable development and customs space. The purpose of the study is to suggest directions for development vectors for a country’s customs space that will mitigate various risks. Originally, 174 countries were selected for analysis, but the final sample was formed by 98 countries. According to the results of econometric modeling, it was determined that the following variables have the greatest impact on the customs space: human development index; GDP per capita; corruption perception index; global enabling trade index; environmental performance index; social progress index; global competitiveness index. The findings can be used by public authorities in developing a strategy for reforming the customs system of developing countries, taking into account the risks and challenges of the global environment. Full article

The Himalayan region serves as a land bridge between China and South Asia but is vulnerable to geopolitical factors. It is important to conduct geopolitical risk assessments to facilitate the restoration and construction of traditional trade routes in the Himalayan region. Based on multisource natural, political, and socioeconomic data, we selected 12 indicators, including topographic relief, landslide risk, multi-hazard index, population density, territorial disputes, conflict risk, corruption perception index, transboundary water disputed risk, night light index, GDP, accessibility, and economic freedom, to assess these risks. A comprehensive assessment of the geopolitical risk in the Himalayan region is presented using the random forest (RF) model, analytic hierarchy process (AHP), entropy weight method, and AHP-entropy weight method. The results indicated that the geopolitical risk in the Himalayan region is generally high in the north and low in the south, with high level of risk primarily concentrated in the Kashmir valley and south, south-central Nepal and southern Tibet, and low level of risk mainly concentrated in the Bhutan and Tibet border areas of China. The high likelihood of natural risk is largely concentrated in the Indian states of Himachal Pradesh and Uttarakhand, Nepal, southeastern Bhutan, and southern Tibet. Significant political risk is mostly confined to the Kashmir valley and its south, while economic risk is mostly concentrated in Khyber-Pakhtunkhwa of Pakistan, Pakistani-administered regions of Kashmir, and Nepal. Geopolitical risk assessment based on the grid scale can better reveal and portray the spatial distribution of geopolitical risk in the Himalayan region and provide a basis for the restoration and construction of traditional trade routes in this region. According to the results of the geopolitical risk assessment, it is recommended that priority be given to construction in areas of relatively low risk, such as those close to Burang Country and Mustang, and that integrated planning be carried out for the restoration and construction of the predominantly low-risk trade routes between China and Bhutan, with further comprehensive analysis of each route conducted in conjunction with field surveys and proposed construction and control strategies. Full article

Corruption represents the misuse of public power by government departments for personal gain, hindering a country’s economic growth. Corruption cannot be eliminated by implementing the national democratic system, and mature democratic countries also exist with varying degrees of corruption. Corruption affects people’s trust in the public sector and the country’s economic development. Open government data can help people understand the governance performance of the government to reduce corruption in the public sector. Citizens can use open government data to generate innovative applications and economic value. This study uses a two-stage data envelopment analysis method to assess the anti-corruption efficiency of 21 countries from 2013 to 2017 through open government data, the corruption perception index, and GDP data. Then, the efficiency analyzed is introduced into the BCG (Boston Consulting Group) matrix to observe the distribution of these 21 countries. Analyzing the results showed that Uruguay and Costa Rica in Central and South America are the two most influential countries in fighting corruption. Turkey is at the bottom in the evaluation of anti-corruption efficiency. In addition, discussions of the included countries for their possible improvement in anti-corruption are also provided by using the association rule’s analysis. The study results will provide a reference for governments to effectively carry out anti-corruption work in the future. Full article

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries. Full article

The aim of this paper is to enhance understanding of factors that undermine sustainable management of renewable resources by identifying and analyzing the main drivers and dynamics involved, with a focus on the role of corruption perceptions and its implications. To shed light on the research question, we chose to perform a comparative study of three different resource sectors in European countries that are ranked differently on the Corruption Perception Index by Transparency International, namely fisheries in Iceland, forestry in Romania, and arable soils in Ukraine. We conducted 40 in-depth semi-structured interviews with various stakeholders to explore assumptions on individual actions and behavior in the sectors. The interviews were analyzed using a qualitative coding procedure based on causal loop diagrams, a method from system dynamics. The results indicate that even though the cases are different, they share a similar outcome, in that privatization of the resource and consolidation of companies took place, along with perceived risk of both unsustainable resource management practices and corruption. Our findings suggest that the underlying similarities of the cases are that privatization occurred around the same time in early 1990s, when neoliberal economic ideology influentially held up the idea that private ownership meant better management. What followed was a transition to economies of scale that ultimately resulted in dominance of large vertically integrated companies in the sectors. The resulting inequalities between large and small actors in the renewable resource management systems serve to increase the risk for unsustainable management decisions as well as increase perceptions of corruption risks, especially amongst smaller actors in the sectors. Full article