This paper analyzes the impact of wage subsidies on lower-skilled formal workers in the Democratic Republic of Congo (DRC). It employs a multi-sectoral, empirically-calibrated general equilibrium model to capture the economy-wide transactions between the formal and informal sectors and assess policy simulations in the DRC. The simulations, both in the short and long run, indicate that when the government provides wage subsidies to lower-skilled workers, it significantly improves the real disposable incomes of both formal and informal households. There is a general increase across formal and informal sectors in real household disposable incomes due to the wage subsidy. The results show that subsidy allocation narrows the income gap between high and low-income households, as well as between formal and informal sectors. The findings are insightful for wage policy simulations, as the wage subsidy targeting lower-skilled formal workers increases real GDP from the expenditure side by 1.19% and 3.19% in the short and long run, respectively, from the baseline economy.

The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.

This study examines consumer attitudes toward cryptocurrencies in Slovakia, focusing on the perceived adequacy of their promotion and the influence of demographic factors such as education, gender, and age. The findings reveal that a significant majority of respondents view cryptocurrency promotion as insufficient, with 77.77% expressing dissatisfaction. Demographic factors were found to have minimal impact on attitudes, suggesting that universal barriers—such as trust, technological literacy, and perceived risks—play a more critical role. Social media emerged as a key platform for engaging consumers, particularly younger demographics, provided that campaigns are well-targeted and informative. These results highlight the need for innovative promotional strategies emphasizing transparency, education, and trust-building to bridge the gap between cryptocurrencies and broader consumer adoption. The study contributes to the growing literature on cryptocurrency marketing by providing actionable insights for addressing challenges in emerging markets like Slovakia.

Studies on the influence of public policies on the regional tourism sector are of high scientific and practical interest, as they offer inputs to guide public management towards strengthening the tourism development of the territories. Through the structural equation model, this study took a sample 99 companies in the tourism sector in Valle del Cauca, Colombia, addressing the relationship between public policy management (PPM) and regional tourism development (RTD), from the perspective of the rational model of business performance. The findings show that the capacity of the state and its entities to comply with the requirements of the organizations, as well as the rigor to take criticism and suggestions for improvement, as a basis to strengthen their management, are the factors that best explain the relationship between the PPM and RTD based on the performance of organizations in the sector, especially focused on increasing market share, productivity, and income. Other findings and practical implications are discussed.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.