Measures Of Corruption Research Articles

Revisiting the Relationship Between Corruption and Press Freedom

ABSTRACTWe use matching methods to explore the relationship between the control of corruption and press freedom from 1996–2016. We find evidence that increases in the control of corruption significantly elevates press freedom over both the four‐year and eight‐year horizon. These effects are strongest for the legal and political environment categories of press freedom. However, we find little evidence that press freedom influences the control of corruption overall, hypothesizing that this lack of an effect could be due to the absence of other complementary institutions (e.g., the rule of law and democracy) and/or because we are using perceptions‐based corruption measurements. We find some evidence that press freedom, paired with a strong rule of law system, can reduce corruption. Our results also suggest that reducing press freedom in countries without such institutions is particularly harmful. Lastly, we find that press freedom reduces voter participation yet increases foreign direct investment, suggesting that the perceptions‐based measures of corruption may not tell the full story.

Three-Dimensional Corruption Metrics: A Proposal for Integrating Frequency, Cost, and Significance

AbstractDespite a lack of consensus on the most effective methods for measuring corruption, a variety of initiatives have been launched over the past decade. This paper contributes to the evolving field by arguing for the integration of frequency, cost, and local significance, into a three-dimensional approach for the development of enhanced metrics. After revisiting the history of corruption measurement, each proposed dimension is discussed by reference to past initiatives and the relevant literature. While the frequency and cost of corruption are discussed based on the varying degrees of attention received over the years, I propose that “significance” represents a third and necessary dimension, capturing the moral severity of corruption within different societal contexts. Taken as a unified whole, the proposed multidimensional approach is argued to have the potential for a more accurate depiction of corruption, enhancing also the clarity and accountability of corruption metrics. By accounting for frequency, cost, and significance, three-dimensional metrics are expected to lay the groundwork for more effective policy responses and deepen our understanding of the complexities inherent in the corruption phenomenon.

Economics of Corruption: Demand Side Case of Western Balkan Countries

Corruption is a very negative phenomenon, which distorts markets and harms economic growth. Corruption has its side of supply and demand. Both supply and demand for corruption are influenced by many factors. The purpose of this article is to identify some macroeconomic and institutional factors that lead to the demand for corruption in the Western Balkans. The Corruption Perception Index and Control of Corruption index are used as measures of corruption, therefore in this paper two models are built, where independent variables are real income per capita, inequality gap, unemployment rate, rule of law, and government efficiency. A panel model, with data for the period 2012-2022 is used to identify the most important variables affecting corruption in the Western Balkans. The results show that the index used to measure corruption affects the statistical significance of the variables, with inequality gap and rule of law being significant in both models. The identification of the factors can serve the governments of these countries to design policies and adopt strategies that will reduce the involvement of people in corrupt practices.

Impact of business cycles on energy poverty: Exploring the significance with sustainable development goals in newly industrialized economies

Energy poverty is a key barrier to achieving SDG 7, which targets full access to affordable and sustainable energy by 2030. Thus, the study utilized the Complementary Percentage Method to track the energy poverty rate in electricity and clean fuel energy for rural and urban areas in NICs from (2000−2021). Subsequently, the study expanded to examine the impact of business cycles on energy poverty in NIC economies. Christiano-Fitzgerald filter and Hodrick-Prescott filter are used to measure the business cycle phases. The findings show that though most NICs have achieved full electricity access, significant disparities remain, particularly in rural areas where millions still lack access to both electricity and clean cooking fuels. Using System-GMM and IV-GMM, the study finds that business cycles, especially recessions, worsen energy poverty in NICs. Economic expansion cycles positively impact energy access and reduce energy poverty. Innovations and investments in the energy sector emerge as positive influencers in alleviating energy poverty. Also, the business cycle reduced renewable energy consumption. Findings indicate that countries with strong governance, effective regulation, rule of law, and control of corruption measures are more successful in reducing energy poverty. The additional transmission estimators reaffirmed findings; income inequality, energy intensity, unemployment and GDP per capita support the outcomes of business cycles' benchmark model. These findings highlight the need for investment in energy infrastructure and targeted policies to close the rural-urban energy gap, particularly for clean cooking fuels, to meet SDGs7.

Every person has their price? Political corruption and CEO compensation

We provide evidence that local political corruption significantly and positively affects CEO compensation, and we demonstrate that this positive association is contingent on asset shielding mechanisms and corporate governance quality. Specifically, corruption appears to inflate CEO compensation to a greater extent in firms with higher cash holdings, paying out dividends, or absence of operating losses. Additionally, the positive correlation between corruption and CEO compensation is more pronounced in firms with less efficient monitoring. Further analysis reveals that, although higher compensation can enhance firm performance, this positive effect is significantly reduced in firms headquartered in more corrupt areas. These findings strongly support the rent extraction explanation of CEO compensation. Our results are robust across different measures of political corruption, variations in model specifications, and assessments of causality.

An empirical study of the level of corruption intolerance among the employees of the Ministry of Internal Affairs

ObjectivesThe report presents the objective need to measure the level of corruption intolerance in the public sector and its importance for the formation of specific behavior aimed at the observance of important value principles in the performance of official duties in the public sector. The activity of the state administration should justify society's expectations of professional competence, legality, loyalty, correctness and responsibility. That is why corruption intolerance can be accepted as a factor for measuring the quality of activity in the public sector. The empirical research on the corruption intolerance in the Ministry of the Intenal Affairs is an important indicator for the efficiency of the system.MethodsThe scientific methodology used is a combination of a theoretical review of the issue, a critical scientific review and the presentation of new scientific advances in the measurement of corruption intolerance.ResultsThe rates of corruption intolerance in the Ministry of Internal Affairs are satisfactory.ConclusionsThere is an objective need to maintain a high level of motivation and effectiveness of training to achieve high-quality professional performance of official duties among the employees of the Ministry of Internal Affairs. Therefore, Dynamic socio-economic changes necessitate a new approach to the problems of measuring the corruption intolerance.

Determinants of experiences and perceptions of corruption: A case study of Russian regions

In this article, the main determinants of experiences and perceptions of corruption in the Russian regions are analyzed. The research focuses on evaluating the determinants of corruption among ordinary people and business representatives in three regions of Russia. The main objectives of the research are: to reveal the relationship of individual characteristics and attitudes toward corruption with citizens’ actual experiences and perceptions of corruption; to learn how experiences and perceptions of corruption influence each other. The main conclusion is that the experience of corruption and its perception are interrelated. Those members of both the general public and the business community who perceive the prevalence of corruption to be high report experiencing corrupt practices more often than those who see a lower frequency of corruption. People who justify corruption are more likely to report encountering bribery and other low-level corruption practices. For business corruption, tolerance of corruption shows a significant link with perception of corruption, but not with real experiences of corruption. Social learning theory and victimization theory can explain the interdependence of perceptions and experiences of corruption in the Russian regions. The indicators of both perception and experience of corruption in the research can be used as indirect measures of corruption. Their interrelation in the same survey may also indicate their validity as instruments for measuring corruption.

Measuring corruption: whose perceptions should we rely on? Evidence from Iceland"

The extent of corruption in Iceland is highly contested. International corruption measures indicate a relatively small amount of corruption while domestic public opinion suggest a serious corruption problem. Thus, uncertainty prevails about the actual extent of corruption and whose perceptions to rely on. This problem is relevant for corruption research in general. Perceptions are increasingly used as proxies for the actual levels of corruption in comparative research. But we still do not know enough about the accuracy of these proxies or the criteria they must meet in order to give dependable results. In fact, radical differences exist concerning evaluations of perceptions between those who believe in unbiased learning and those believing perceptual bias to be widespread. The purpose of this article is, therefore, to attempt to gauge which factors may influence how perceptions of corruption are shaped and why differences in corruption perceptions between different groups may be so pronounced. We present findings from original survey data from three parallel surveys – among the "public", experts, and "municipal practitioners" – conducted in Iceland in 2014. Expectations based on the perceptual bias approach are tested, indicating that perceptions may be affected by (1) information factors, (2) direct experience of corruption and (3) emotive factors. The validity of perception measures should be considered with this in mind. Domestic experts are likely to be well informed and avoid perceptual bias to a greater extent than other groups. Our examination of the Icelandic case suggests that the Corruption Perception Index (CPI) tends to underestimate corruption problems in "mature welfare states", such as Iceland, whilst the general public tends to overestimate it.

Corruption and insider trading

We investigate firm corruption in China by extracting a measure of corruption from published financial statements and use this to demonstrate that corruption impacts the trading decisions of insiders. Specifically, we show that insiders in firms that are more corrupt trade more aggressively, and they are more willing to trade on their private information as evidenced by the increased informativeness of their trades, in respect of both purchases and sales. This link between firm corruption and trade informativeness is robust to the inclusion of a number of factors that are known to influence the informativeness of such trades, including trade characteristics, insider characteristics and the firm's information environment. We also consider the effect of the appointment of a new CEO or Chair. Overall, corruption related trade informativeness holds consistently for both purchases and sales. Finally, we show that this measure of corruption is robust to the inclusion of several alternative indicators of corporate misconduct.

On the utility of partially corrupted flow measurement data arising from adjacent acoustic Doppler current profilers for energy yield assessment

Recommended practice for quantifying the energy resource at a tidal energy site requires the use of multiple instruments deployed across the site. However, the instruments used work by emitting an acoustic pulse and instruments working at the same time have the potential to interfere with each other through a process known as ’cross-talk’. It is important to understand the impact of cross-talk on measurements and how this can be managed and through data processing or suitable positioning of devices. The ReDAPT project conducted a measurement campaign using two Acoustic Doppler Current Profilers (ADCPs) placed upstream of an operational tidal turbine. This aimed to assess the ’in-line’ instrument placement guidelines from IEC 62600-200 for Power Performance Assessment (PPA) in real-world conditions. Consequently, the results within hold potential to support arguments for expanding these zones or adjusting their general dimensions. Despite adhering to industry standards and best practices to eliminate unreliable data in the Quality Control (QC) checks, in both concurrently measuring ADCPs at different time stamps in approximately 15 % of the returned data. This work identified for the first time interference throughout the campaign and quantified subsequent impact on estimates. A method to remove data anomalies caused by interference between closely positioned ADCPs has been developed and demonstrated, resulting in a 7 % variation in estimated Annual Energy Production (AEP). The algorithm effectively removed approximately 90 % of the corrupted measurements. Moving forward, multi-sensor deployments could use the algorithm described to double-check for interference within the data sets, although care should be taken to avoid this by choosing a suitable layout for deployment.

Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be 26%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$26\\%$$\\end{document} higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.

Uncovering the Hidden Variable: Corruption and the Underperformance of Zimbabwean Soccer Teams

This study investigates the factors contributing to the underperformance of Zimbabwean soccer teams, with a specific focus on the often-overlooked variable of corruption. Through a mixed-methods approach, combining statistical analysis and qualitative interviews, we identify the determinants of soccer success and propose strategies to address maladministration. Our findings suggest that corruption, alongside economic performance, talent pool size, experience, culture, and political factors, plays a significant role in national soccer team performance. However, the literature lacks exploration of these factors in the African context. Our research reveals a lack of clear strategic planning processes, inadequate governance structures, and limited financial incentives in Zimbabwean soccer clubs. We recommend education and training, business coaching, ethical leadership, transparency, accountability, and punitive measures for corruption to improve soccer development in Zimbabwe. This study contributes to the understanding of soccer development in Africa and highlights the need for urgent reforms to address corruption and maladministration in sports management.

Robust Corrupted Data Recovery and Clustering via Generalized Transformed Tensor Low-Rank Representation.

Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted tensor data and make it compatible with the downstream clustering task remains a challenging problem. In this article, we study a generalized transformed tensor low-rank representation (TTLRR) model for simultaneously recovering and clustering the corrupted tensor data. The core idea is to find the latent low-rank tensor structure from the corrupted measurements using the transformed tensor singular value decomposition (SVD). Theoretically, we prove that TTLRR can recover the clean tensor data with a high probability guarantee under mild conditions. Furthermore, by using the transform adaptively learning from the data itself, the proposed TTLRR model can approximately represent and exploit the intrinsic subspace and seek out the cluster structure of the tensor data precisely. An effective algorithm is designed to solve the proposed model under the alternating direction method of multipliers (ADMMs) algorithm framework. The effectiveness and superiority of the proposed method against the compared methods are showcased over different tasks, including video/face data recovery and face/object/scene data clustering.

Revolutions and corruption

We investigate the relationship between successful revolutions and corruption using data on revolutionary campaigns since 1900 and corruption measures retrieved from the Varieties of Democracy database. We find that successful nonviolent and violent revolutions produce null effects on corruption; education decreases corruption; and upon adjusting for the moderating effect of education, violent revolutions induce corruption. Our results imply that classic narratives celebrating such upheavals as corruption-limiting are oversimplified and optimistic. Our analysis challenges conventional wisdom and contributes an instructive, empirically-grounded assessment of the revolution’s corruption consequences to the scholarship.

Unveiling a novel approach to corruption measurement: Leveraging household survey data on income and expenditure through forensic analysis

Corruption exacerbates and perpetuates the global problems of poverty and inequality. However, accurately quantifying corruption, a prerequisite for any sensible analysis and policy formulation, is hindered by its clandestine nature and the reticence of involved parties. To overcome these obstacles, we conduct a forensic investigation to uncover the extent of bureaucratic corruption in Vietnam. Leveraging self-reported income and expenditure data from a nationally representative dataset and an instrumental variable approach, our analysis reveals that bureaucratic corruption accounted for 2.68% of the country's GDP in 2018, with variations observed across government sectors and with seniority levels. We identify inadequate remuneration, ambiguous regulations, and inconsistent enforcement as primary drivers of corruption, particularly in the education sector. Validating the findings with a unique dataset on extra-class expenditure, we demonstrate the empirical plausibility of our econometric estimates. This methodology can leverage a more reliable and comparable cross-countries corruption database.

Slave trade, trust, and corruption

ABSTRACT Theory predicts that societies with higher social capital have lower levels of government corruption. This hypothesis has been difficult to evaluate empirically because of widespread reliance on aggregated, country-level measures of trust and corruption, and reverse causality. We utilize micro-level international data from 34 African nations and two alternative measures of both trust and corruption to re-evaluate the evidence of this theorized relationship. To account for reverse causality, we use historical transatlantic slave exportation data as an instrumental variable. Slave trade has historically lowered interpersonal trust in Africa, which has had a long lasting effect on past and present levels of corruption. Our findings indicate that a higher level of personalized trust leads to more corruption, whereas a higher level of generalized trust leads to less corruption. This result is robust to alternative specifications of regression models, including different measures of trust and experienced versus perceived types of corruption.

Anti-Corruption Efforts in the Healthcare Sector During the Covid-19 Pandemic in Malaysia

The Covid-19 pandemic has had an irreversible impact on the global economy and industries, particularly in the healthcare sector. The rush to respond to the pandemic, particularly in terms of getting treatment and vaccines and technology to market, has created a huge opportunity for undisclosed corruption and misconduct in the research and development and procurement processes. Effectively responding to current and future global health corruption threats is critical if the world is to provide health care to all. Due to a lack of research in preventive corruption measures in the healthcare industry, this paper aims to provide an overview of anti-corruption efforts among healthcare companies listed on the main board of Bursa Malaysia. The disclosure index is made up of 47 items derived from a review of previous literature and the Malaysian Anti-Corruption Act. The content analysis research method is used to analyze information from 13 companies listed in 2020’s anti-corruption policies and procedures published on their website, board charter, whistleblowing policy, code of ethics and conduct, annual report, sustainability report, and corporate governance report. According to the findings, Malaysia’s healthcare industry has demonstrated a strong commitment to fighting corruption, with 76.9% of companies scoring higher than average on the disclosure index.

ASSESSING THE IMPACT OF CORRUPTION ON INCOME INEQUALITY

This article is devoted to the study of the impact of corruption on income distribution. The study reveals statistically significant associations between the level of corruption and income inequality in Kazakhstan by applying least squares linear regression models. The paper uses the Corruption Perception Index as a measure of corruption and the Gini Index as a measure of income inequality. The paper also analyzes the impact of corruption on income inequality in other countries. This made it possible to determine the specifics of such an impact in countries with different levels of economic development. The results of the study showed that a decrease in the level of corruption, expressed as an improvement in the Corruption Perception Index, is associated with a decrease in income inequality, expressed as the Gini Index. The study also found that population growth has high statistical significance and is associated with a significant reduction in the level of social inequality in Kazakhstan. This confirms that population growth is predominantly observed among the less well-off strata of society, which leads to an increase in the Gini coefficient.

LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation

A fundamental task in the realms of computer vision, Low-Rank Matrix Recovery (LRMR) focuses on the inherent low-rank structure precise recovery from incomplete data and/or corrupted measurements given that the rank is a known prior or accurately estimated. However, it remains challenging for existing rank estimation methods to accurately estimate the rank of an ill-conditioned matrix. Also, existing LRMR optimization methods are heavily dependent on the chosen parameters, and are therefore difficult to adapt to different situations. Addressing these issues, A novel LEarning-based low-rank matrix recovery with Rank Estimation (LERE) is proposed. More specifically, considering the characteristics of the Gerschgorin disk's center and radius, a new heuristic decision rule in the Gerschgorin Disk Theorem is significantly enhanced and the low-rank boundary can be exactly located, which leads to a marked improvement in the accuracy of rank estimation. According to the estimated rank, we select row and column sub-matrices from the observation matrix by uniformly random sampling. A 17-iteration feedforward-recurrent-mixed neural network is then adapted to learn the parameters in the sub-matrix recovery processing. Finally, by the correlation of the row sub-matrix and column sub-matrix, LERE successfully recovers the underlying low-rank matrix. Overall, LERE is more efficient and robust than existing LRMR methods. Experimental results demonstrate that LERE surpasses state-of-the-art (SOTA) methods. The code for this work is accessible at https://github.com/zhengqinxu/LERE.

A stochastic ADMM algorithm for large-scale ptychography with weighted difference of anisotropic and isotropic total variation

Ptychography, a prevalent imaging technique in fields such as biology and optics, poses substantial challenges in its reconstruction process, characterized by nonconvexity and large-scale requirements. This paper presents a novel approach by introducing a class of variational models that incorporate the weighted difference of anisotropic–isotropic total variation. This formulation enables the handling of measurements corrupted by Gaussian or Poisson noise, effectively addressing the nonconvex challenge. To tackle the large-scale nature of the problem, we propose an efficient stochastic alternating direction method of multipliers, which guarantees convergence under mild conditions. Numerical experiments validate the superiority of our approach by demonstrating its capability to successfully reconstruct complex-valued images, especially in recovering the phase components even in the presence of highly corrupted measurements.