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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. [ABSTRACT FROM AUTHOR]

From an ethical standpoint, combating corruption is crucial for promoting justice and equality. The rule of law and ethical governance involves clear standards of behavior for public servants and mechanisms for ensuring these standards are upheld. Corruption undermines public trust in government and democratic institutions and exacerbates social inequality and injustice (disproportionately affects the poor and marginalized groups, denying them access to essential services and opportunities). The fight against corruption during a full-scale war has ethical and purely pragmatic implications for Ukraine, the data of which formed the basis of this study. The lack of tangible progress in the fight against corruption in general, the lack of transparency of many institutional mechanisms in public administration, the revealed corruption schemes in the distribution of international military and humanitarian aid, as well as in the field of public defence procurement, the lack of punishment for corrupt officials in the highest echelons of power threaten the loyalty of international donors and allies, reduce Ukraine’s authority in the international arena, slows down Ukraine’s movement towards the EU, and significantly affects the decisions of foreign partners. The article demonstrates the results of forecasting the future level of corruption in Ukraine (for 2024‒2027) based on the retrospective dynamics of the Corruption Perceptions Index by Transparency International for 1998‒2023. Two economic and mathematical models are used for forecasting: Autoregressive Integrated Moving Average (ARIMA), which better reflects long-term historical trends and fluctuations, and the exponential smoothing method, which is more sensitive to the latest values of the time series. The statistical analysis package STATISTICA was used for the calculations. The forecasting results are disappointing since both methods showed an expected decrease in the level of corruption in 2024-2027, but in critically low volumes: by 1 point according to the ARIMA model and by 3-4 points according to the exponential smoothing method. The results of this study can serve as a basis for public advocacy campaigns as an argument for the need to radically revise the existing format of anti-corruption policy in Ukraine given its European future.

This ground-breaking research investigates the impact of the logistics performance index (LPI), corruption perception index (CPI), foreign direct investment (FDI), and per capita gross domestic product (PGDP) on the global competitiveness index (GCI). This research is motivated by the willingness to uncover the collective impact of the considered independent variables that strengthen the global competitiveness of countries in the world region. Using cross-sectional data from 110 countries in 2018, the study employed the forward stepwise technique of Ordered Probit Regression Model to analyse the impact of aforementioned variables towards the categorised levels of GCI unveiling varying impacts on high, moderate and low-ranked nations. The research findings revealed that logistic performance, FDI and PGDP significantly contribute to global competitiveness, while the impact of CPI deemed insignificant. This research will contribute to the existing literature by providing theoretical and empirical insights into decision-making processes related to global competitiveness. Furthermore, the results will offer valuable information to policymakers and regulatory authorities seeking to enhance competitiveness on a global scale. [ABSTRACT FROM AUTHOR]

The article is devoted to the topical issues of increasing the corporate social responsibility of business through the introduction of anti-corruption audits at enterprises, the main component of which is analytical procedures. The purpose of the article is to develop a set of analytical procedures in the anti-corruption audit system to ensure the economic security of business entities for individual business processes in order to identify problem areas of the formation of individual corruption risks. As a result of the conducted research, the economic essence of the concept of "analytical procedures" was clarified and their place in the anti-corruption audit system to ensure the economic security of the enterprise was determined; trends of changes in the corruption index in Ukraine were analyzed and a predictive model of its level in the near future was developed; the main areas of risk of corruption that arise in the course of the activity of business structures are substantiated; business indicators that indicate the probability of corruption at the enterprise are determined; analytical procedures for the detection of fraudulent and corrupt actions in the context of individual business processes: procurement, production, sales and in the management system as a whole are systematized. For each analytical procedure, an algorithm for its implementation has been developed, indicating the appropriate methodical techniques. It has been proven that analytical procedures are, on the one hand, an independent block of methods for obtaining audit evidence, and on the other, a part of analytical work that must be carried out both within the scope of audit activity in general and in the process of enterprise management to increase the social responsibility of business and ensuring economic security. The proposed analytical procedures of the anti-corruption audit make it possible to identify corruption schemes in the relevant business processes, to focus the attention of auditors on the relevant areas of corruption risk, ensure the economic security of the enterprise, contribute to the minimization of corruption risks at enterprises, create an analytical basis for the formation of an anti-corruption program of the enterprise, which ultimately lays the foundation for improving the efficiency of the enterprise, sustainable development and improving its business reputation. [ABSTRACT FROM AUTHOR]

AbstractThis study aims to investigate the relationship between public governance and the Corruption Perception Index (CPI) while also examining the impact of national income per capita on this association. This study covers a decade-long period from 2012 to 2021 and involves 180 countries from various regions, including America, Asia Pacific, Middle East and North Africa (MENA), Sub-Saharan Africa, Europe Central Asia, and Western Europe. To analyze the extensive dataset, which comprises 1768 data points, the study used the Generalized Estimating Equations (GEE) method. The research findings suggest that all aspects of public governance, such as voice and accountability, political stability, government effectiveness, regulatory quality, the rule of law, and control of corruption, significantly influence the perception of corruption. This indicates that good governance practices can effectively reduce corruption. Interestingly, the impact of income per capita is not uniform across all dimensions of governance and the CPI. Instead, the study reveals that stricter rule of law and anti-corruption measures are more influential in wealthier nations, highlighting the importance of proper resources and infrastructure in eradicating corrupt practices. These insights are particularly valuable for policymakers, government officials, and international organizations involved in promoting good governance and fighting corruption. The results of this study can be used to formulate effective policies and strategies for strengthening governance practices, reducing corruption, and ensuring a fair and just society.

The interdependency of various areas of Statistics is gaining good attention in the literature. This is possible through innovations such as the log-transformation of distribution to a parametric regression model hence integrating contemporary probability distribution models with the classical regression method. The new model is essentially preferred due to its applicability in wider scenarios. In this article, the base distribution is the Power Chris-Jerry distribution. The reparametrized regression model equivalent was carefully derived with the maximum estimation procedure under censored sample also considered. A simulation study of the log-power Chris-Jerry regression model was carried out with measures of performance presented. The COVID-19 patient lifetime censored data was deployed to justify the motivation for developing the new model and twelve competing models were used to compare the proposed regression. The results show that the proposed model is indeed better and preferred to its competitors.

Purpose: As a consequence of the proposal of the Carbon Neutral and Carbon Peak policy in 2020, the Chinese Government is paying more attention to developing sustainability performance. This study aims to assess the direct influence of country-level and corporate anti-corruption measures on environmental, social and governance (ESG) and its three dimensions, besides ascertaining the moderating role of firm size. Design/methodology/approach: This study used the system generalized method of moments on a sample of 820 Chinese listed firms from 2012 to 2021. Findings: The findings show that country-level and corporate corruption negatively affect ESG performance. Corporate anti-corruption measures have a more pronounced positive influence on the sustainability performance of small firms than large firms due to the limited resources, lower political position and weaker refusal power of small firms. Research limitations/implications: The study has great implications for governments, corporate boards and ESG rating agencies. Government and corporate boards should mitigate the risks of country-level and corporate corruption to attain sustainable development goals. Rating agencies should add country-level and corporate corruption into the ESG evaluation system. Originality/value: Some empirical results have proven that anti-corruption measures help reduce the emission of carbon dioxide, but few evidence shows how country-level and corporate corruption affect ESG and its three dimensions. [ABSTRACT FROM AUTHOR]

Purpose: This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018. Design/methodology/approach: A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency. Findings: This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures. Originality/value: The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems. [ABSTRACT FROM AUTHOR]

From an ethical standpoint, combating corruption is crucial for promoting justice and equality. The rule of law and ethical governance involves clear standards of behavior for public servants and mechanisms for ensuring these standards are upheld. Corruption undermines public trust in government and democratic institutions and exacerbates social inequality and injustice (disproportionately affects the poor and marginalized groups, denying them access to essential services and opportunities). The fight against corruption during a full-scale war has ethical and purely pragmatic implications for Ukraine, the data of which formed the basis of this study. The lack of tangible progress in the fight against corruption in general, the lack of transparency of many institutional mechanisms in public administration, the revealed corruption schemes in the distribution of international military and humanitarian aid, as well as in the field of public defence procurement, the lack of punishment for corrupt officials in the highest echelons of power threaten the loyalty of international donors and allies, reduce Ukraine's authority in the international arena, slows down Ukraine's movement towards the EU, and significantly affects the decisions of foreign partners. The article demonstrates the results of forecasting the future level of corruption in Ukraine (for 2024-2027) based on the retrospective dynamics of the Corruption Perceptions Index by Transparency International for 1998-2023. Two economic and mathematical models are used for forecasting: Autoregressive Integrated Moving Average (ARIMA), which better reflects long-term historical trends and fluctuations, and the exponential smoothing method, which is more sensitive to the latest values of the time series. The statistical analysis package STATISTICA was used for the calculations. The forecasting results are disappointing since both methods showed an expected decrease in the level of corruption in 2024-2027, but in critically low volumes: by 1 point according to the ARIMA model and by 3-4 points according to the exponential smoothing method. The results of this study can serve as a basis for public advocacy campaigns as an argument for the need to radically revise the existing format of anti-corruption policy in Ukraine given its European future. [ABSTRACT FROM AUTHOR]

This research aims to analyze the influence of economic growth, foreign direct investment, corruption perception index, consumer price index, and human development index on unemployment in ASEAN from 2018 to 2022. This research is quantitative in nature, utilizing panel data analysis. The population in this study consists of the 11 ASEAN member countries with annual data from 2018 to 2022. Data were obtained by accessing the websites of the World Bank, Transparency International, UNDP, and Trading Economics. Subsequently, the data were processed using Eviews 12 software. The results of the study employing the Fixed Effects Model (FEM) demonstrate that unemployment is negatively influenced by economic growth, consumer price index, and human development index, whereas it is positively affected by the corruption perception index. Conversely, foreign direct investment does not exert a noteworthy impact on unemployment within the ASEAN region. Research on unemployment in ASEAN countries has been inconsistent; further investigation is needed to address this gap. Factors contributing to unemployment, particularly within this region's context, require deeper understanding. Additionally, this research also encompasses a new dimension: the impact of corruption, which has been underexplored previously.

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.

The article is devoted to the study of behavioural factors that influence the decision-making of business entities and the population regarding tax evasion. The problem of tax evasion from the point of view of the influence of psychological characteristics of the individual and external economic factors on the deviant behaviour of taxpayers is considered. The influence of these factors on the volume of the shadow economy in the country is proven. The research allowed us to outline the main models that explain the reasons and motivations of taxpayers when they decide to violate tax legislation. It is emphasized that tax offences as a component of the shadow economy are present in many countries of the world. It is noted that the volume of violations increases with the onset of destructive events in the economy, such as economic and social crises and war. It is stressed that a significant role in the presence of deviant behaviour of taxpayers is played by their financial and legislative ignorance. The main elements of taxes that pose the greatest risks of tax evasion are identified. It has been found that the level of corruption in the country and the insufficient severity of possible punishment for tax crimes play a significant role in the decision of economic entities to evade taxation. An analysis of the corruption perception index in some countries has been made. It is proved that in less corrupt countries, tax collection is higher than in countries with high levels of corruption. It is emphasized that the lack of proper control by tax authorities leads to a larger scale of tax evasion, so it is necessary to conduct all legally established types of tax audits, despite the martial law in the country.

Copyright of Riwayat: Educational Journal of History & Humanities is the property of Riwayat: Educational Journal of History & Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most prominent and well-known global index of corruption is the Corruption Perception Index (CPI), released annually by Transparency International (TI). The model we propose is the first approach to explore the quantile relationship between CPI rankings in consecutive years. This study utilizes the quantile causality approach to investigate whether corruption levels impact the following years over the 2012–2019 period. We show that CPI itself has a significant impact on the forthcoming years. It is not easy to change the scores of corruption in the most corrupt countries and the least corrupt countries. However, in countries with moderate corruption, the evaluation of corruption may be significantly improved (or worsened). To our best knowledge, the phenomenon is rarely discussed in the literature and is also an important contribution. [ABSTRACT FROM AUTHOR]

The curse of natural resources is an emerging phenomenon that is of interest to economic researchers. The attention of researchers is about the direct effects of resources on economic factors. The subjects of these researches are around axes such as the impact on growth, productivity, and production incentives. In the research conducted, special attention has been paid to how the abundance of resources affects macroeconomic factors such as the Dutch disease; but what is not considered is the investigation and analysis of the side effects of abundant resources on economic, social, and political indicators. One of the side effects of the abundance of resources can be its effect on the spread of economic corruption. In case of an abundance of natural resources, the potential for widespread corruption is created. This research was done in a descriptive and analytical way. It has investigated the relationship between the extent of economic corruption and the abundance of natural resources. To conduct this research, the statistics of selected developing countries in the period of 2000-2021 have been used. The information used in this research is the indicators of the perception of corruption, fuel export of countries, and economic freedom during the research period, which is from reliable sources such as the United Nations, Transparency International, and the Heritage Foundation. Multivariate panel data regression method with random effects has been used to estimate model parameters. The results of this research show that there is a negative and significant relationship between the corruption perception index and the fuel export index of selected countries, so that in the 95% confidence interval, the estimated coefficient of the independent variable is negative 0.01. For economic freedom, it is 0.07, which is in line with the results of other researches in this field. Therefore, it can be stated that the abundance of natural resources has a direct relationship with the extent of economic corruption.

The study investigates the impact of corruption, market capitalization, exports, and foreign direct investment on the wealth of 178 countries worldwide. Thus, the paper uses univariate and multivariate regressions to observe the nexus among exports, foreign direct investment, market capitalization, corruption, and wealth of nations. The findings indicate that corruption poses a significant hindrance to prosperity and development, as evaluated with respect to the Transparency International Corruption Perceptions Index. Additionally, the results showed that the world’s poorest nations are becoming less corrupt while the wealthiest ones are growing more corrupt. The paper also concludes that exports and market capitalization are critical for prosperity and development when combined with lower corruption levels. Furthermore, the analysis also suggests that inbound foreign direct investment favors the development of emerging countries. Surprisingly, market capitalization and exports had little impact on wealth of countries before the crisis period. Moreover, integrity also fosters economic growth. Overall, the study concludes that the causes of wealth are country-specific. AcknowledgmentsI thank the editor and the reviewers for the helpful comments and suggestions that significantly enhanced this work. The usual disclaimer applies.

The article considers new approaches to the creation of a corruption research system. The authors point out that modern economic and legal methods for calculating corruption indices either do not take into account all manifestations of corruption or are highly influenced by political factors. The authors substantiate the need to apply a unified methodology for studying the level of corruption within the framework of the EAEU.Aim. Propose at the EAEU level the use of a comprehensive economic and legal methodology for studying the level of corruption, which, along with traditional methods, involves the active use of an artificial intelligence system — storage, transmission and processing of big data to detect corruption manifestations.Tasks. Determine the legal and economic foundations for studying the level of corruption in the Russian Federation and in the EAEU countries, as well as identify legal problems that impede the creation of uniform standards for studying corruption in the EAEU countries.Methods. In this study, methods of comparative law, methods of analysis and synthesis, as well as methods of deduction and induction were used.Results. In the course of the study, the authors come to the conclusion that there are a number of problematic economic and legal aspects both at the national level and at the international level in the field of corruption research. These problems are expressed in the absence of unified national and international standards that allow an impartial assessment of the level of corruption in the EAEU countries.Conclusion. As a result of the analysis of the identified problems, the authors propose ways to solve them through the creation of unified information economic and legal systems in the field of corruption research within the framework of the EAEU.

Starting from the premise that the impact of foreign direct investment (FDI) on the economies of the host countries is different from one economy to another, the aim is to evaluate the impact of a set of economic and social indicators on FDI and net income (% GDP–Gross Domestic Product) in emerging, ex‐socialist countries of the European Union. Using econometric models, we analysed the relationship between the evolution of the net FDI inflows and a number of statistical indicators. Our findings proved that there are similarities between the countries analysed, in terms of the evolution of net FDI inflows, with some differences being recorded for Hungary, where the evolution of net FDI inflows has major fluctuations. We found that the countries present both similarities and differences in terms of variables that affect FDI. The FDI net inflows (% GDP) are in seven, out of nine economies, positively influenced by GDP, as it follows: Bulgaria, Lithuania and Slovenia are FDI attractive by an increasing GDP rate, while Latvia, Poland and Romania react to a better GDP per capita. Hungary is the only one that is positively influenced by both GDP rate and GDP per capita. Moreover, a decreasing corruption perception index, country risk rating, income tax (% of commercial profit) and other taxes paid by companies (% commercial profit) can positively influence the inflows of FDIs in some of the analysed countries. [ABSTRACT FROM AUTHOR]

This article empirically evaluates the impact of corruption on the level of non-performing loans (NPLs) using the international bank-level data, spanning over the period 2000-2016 and across 140 countries. We find a positive and statistically significant relationship between corruption and NPLs. We also analyze the channels through which corruption affects NPLs. We find that the relationship between corruption and NPLs becomes more pronounced during and after the global financial crisis and is more pronounced for smaller banks. The association between corruption and NPLs is stronger in countries characterized by a high level of collectivism. The link between corruption and NPLs is higher where the legal environment is weak and where economies are market-based. [ABSTRACT FROM AUTHOR]

This article empirically evaluates the impact of corruption on the level of non-performing loans (NPLs) using the international bank-level data, spanning over the period 2000–2016 and across 140 countries. We find a positive and statistically significant relationship between corruption and NPLs. We also analyze the channels through which corruption affects NPLs. We find that the relationship between corruption and NPLs becomes more pronounced during and after the global financial crisis and is more pronounced for smaller banks. The association between corruption and NPLs is stronger in countries characterized by a high level of collectivism. The link between corruption and NPLs is higher where the legal environment is weak and where economies are market-based.

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. [ABSTRACT FROM AUTHOR]

The purpose of this research is to study the effects of democracy and corruption on attracting foreign direct investment in West and East Asia. To do this, a dynamic panel model and generalized method of moments are employed over the period 2009-2018. The results of the estimates for the 13 countries of the West Asia Group indicate a positive and significant relationship between the democracy and foreign direct investment and the existence of a significant negative correlation between corruption and the consumer price index with the foreign direct investment. Also, in this group of countries, the effects of the degree of trade openness, democracy and real exchange rate on foreign direct investment were positive and significant and the effects of corruption and consumer price index were negative and significant. Besides, according to the results of this method for 15 East Asian countries, the corruption and consumer price index had negative and significant effects, and the democracy, corruption, trade openness, economic growth and real exchange rate had positive and significant impacts on foreign direct investment. Therefore, regarding the effect of democracy on attracting foreign direct investment, Jensen's theory was approved in both groups of countries, but regarding the effect of corruption on FDI, in East Asia the theory of helping hand of corruption and in West Asia the theory of destructive hand or the corruption was confirmed. According to the results, the impact of these variables on foreign direct investment in the East Asian group was greater than the West Asian group.

Copyright of R&S: Research Studies Anatolia Journal is the property of Dr. Arif Yildiz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Selcuk University Social Sciences Institute Journal is the property of Selcuk University Social Sciences Institute Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examined the long-run relationship between stock market price and some macroeconomic indices in Nigeria. The time series data on stock market price, foreign exchange rate, inflation rate, broad money supply, corruption perception index, and credit to private sector between 1980 and 2021 were used for the study. The data were subjected to diagnostic tests before estimation. To this end, the autoregressive distributed lag method was used to estimate the model. The results of unit root and Johansen co-integration tests showed that stock market price in Nigeria is majorly influenced by macroeconomic factors in the long-run. All the included explanatory variables were found to have a significant effect on stock market price, except inflation and corruption perception index. It is recommended that the monetary authorities and policy makers should be more concerned about the changes in inflation rate due to its negative impact on stock price in Nigeria. To this end, unproductive borrowing by banking sector and/or public expenditure on unproductive items should be discouraged and, businessmen should pay attention to movement of foreign exchange rate, broad money supply and credit to private sector in their decision to invest in stock market in Nigeria. [ABSTRACT FROM AUTHOR]

Corruption remains one of the main problems of Post-Soviet states. Georgia, Belarus, Lithuania, and Latvia are doing relatively well compared to other Post-Soviet states. Popular explanations for high corruption rates are underdevelopment of democratic institutions and the limitation of human rights. However, the lack of women’s participation can be also another factor that could explain the widespread corruption level. According to popular stereotypes, women are considered as more honest and “fairer” gender, which has an impact on the decrease in corruption rate. There is a belief that women have a perception of risk aversion which makes them less likely to engage in corrupt activities. There is no consensus regarding whether women’s participation has an effect on reducing corruption. The purpose of this paper is to test to what extent women’s participation in parliament, school and or with tertiary education, labor force affects corruption rate in Post-Soviet states. The SPSS software was used to assess a relationship between aforementioned variables. According to findings, there is a strong, positive, and statistically significant impact of women enrollment in school and/or with tertiary education on a country’s score in the Corruption Perception Index. I argue that promoting women’s education is the best way to lower corruption in the post-Soviet states.

The study focuses on the relationship between the corruption perception index and mechanisms of corporate governance. We focus on a sample of 130 countries that are included in the data provided by the World Economic Forum and Transparency International from 2006 to 2017. Current research provides empirical evidence that the strength of investors' protection and efficacy of corporate boards help control corruption and the country level. A panel regression model was used to examine the hypothetical relationships. The findings of the study suggest the negative relationship between the efficacy of corporate boards and the corruption perception index. Similarly, we found the negative impact of the strength of investor protection on the corruption perception index. Hence the results indicated that two of the above-mentioned corporate governance mechanisms i.e. the strength of investors' protection and efficacy of corporate boards help reduce corruption at the country level. The policymakers should continue to strengthen the legislation of corporate governance, and the management authorities should adopt corporate governance practices in the true spirit. Hence this paper contributes to existing research on corporate governance and corruption by taking the global perspective. [ABSTRACT FROM AUTHOR]

Although Pakistan receives large quantity of foreign aid, like other developing countries, but it remains more dependent on foreign assistance for economic development since independence. This situation has commenced a vigorous discussion on aid-growth effectiveness. This research work evaluates the macroeconomic impact of foreign aid on Pakistan economy by using secondary data. The empirical analysis is based on ARDL cointegration approach after testing for unit root, using the data for the period 1972-2014. The findings suggest there is no long run relationship between Foreign aid and Economic Growth. However, there exists negative short run relation between Foreign aid and Economic Growth of Pakistan. Based on the study findings, the study recommends that government of Pakistan should find alternate sources of financing as the relation between foreign aid and economic growth is found negative and insignificant. The in depth analysis of the study made it evident that allocation of aid to those sectors of the economy which really needs development, is more productive, provided that the country should use aid funds in the right direction, as corruption less economy prosper more rapidly.

The first objective of this study is to demonstrate that the UN sustainable development goals (SDGs) remain beyond reach for the world's poorest economies by analyzing the trends in the worldwide governance indicators (WGIs), corruption perception index (CPI), the GDP per capita, and the human development index (HDI) using the case of 10 former European colonies (e.g., Burundi, Burkina Faso, Central African Republic, Guinea‐Bissau, Haiti, Madagascar, Niger, Sierra Leone, Togo, and the Democratic Republic of Congo). Second, this study compares three‐panel regression models (pooled OLS model, fixed‐effects model, and random effects model) to examine the influence of the WGIs on CPI, GDP per capita, and HDI and then derive the most appropriate model. Based on the results of these two objectives, a theoretical framework is drawn to provide specific recommendations to overcome some of the challenges to sustainable development in the selected countries. [ABSTRACT FROM AUTHOR]

The article presents the results of applying data analysis methods, in particular cluster analysis and machine learning methods for corruption analysis. The analysis of dependence of the corruption perception index on social and economic indicators in different countries of the world was carried out. Data was collected for the formation of the feature space, the most significant features that have the greatest impact on the corruption perception index were selected, and the countries of the world were clustered according to the selected features. A scheme for conducting a comprehensive analysis to identify the most significant signs and causes affecting the corruption perception index has been developed. Based on the results of clustering, a classification model that can predict the level of corruption in the country based on the values of selected attributes was trained. It is based on the composition of Bagging algorithms with decision trees as basic classifiers. The results of the study can be used by public authorities to develop, organize and adopt appropriate counteraction measures. A software tool based on in the Python language, which allows to perform appropriate analysis using updated data was developed. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the influence of corruption on the inflow of foreign direct investment (FDI) in Georgia, Armenia and Azerbaijan, from 2012 till 2018. The study attempts to answer the question: “What effect does corruption have upon the volume of foreign direct investment inflow to a country?” Using the statistical data from UNCTAD for foreign direct investment and Corruption Perception Index from Transparency International, for a period of 7 years, 2012-2018, we assess the specific influence of corruption on FDI using GDP as the control variable. A regression model was developed to establish the relationship between FDI and Corruption Perception Index. Co-integrating Regression analysis was carried out using relevant econometric techniques. The model is based on a theoretical approach found in relevant economic literature. The paper findings confirm the majority of modern literature and show a negative significant correlation between the variables analyzed, but at a lower intensity than expected.

Corruption is a highly debated issue that harms both the governments of individual countries as well as citizens themselves. Corruption in the public sector can lead to an increase in public spending and a reduction in the amount of taxes levied, thus increasing fiscal deficits and creating macroeconomic instability. Utilizing electronic administration within the public sector has recently been given much attention a key tool for fighting corruption in public administration. E‑administration not only makes it possible to provide more information to the population more simply, efficiently, and quickly, but it also helps to eliminate discretion on the part of officials, i.e., the officials’ power to make decisions based on their personal assessment of a situation. This paper focuses on the relationship between a country’s corruption level and the degree of e‑government being used in the country. Using graphical interpretation of statistical data and regression analysis, it was found that utilizing e‑government within public administration has a positive effect on a country’s corruption level. However, economic performance demonstrated the greatest influence on the corruption level for the given period and group of countries.