Earnings disparities in South Africa, and specifically the Eastern Cape region are influenced by a complex interplay of historical, socio-economic, and demographic factors. Despite significant progress since the end of apartheid, persistent disparities in earnings continue to raise questions about the effectiveness of policies aimed at reducing inequality and promoting equitable social system. Individual-level dataset from the 2021 South African general household survey were subjected to exploratory analysis, while Heckman selection model was used to investigate the determinants of earnings disparities in the study area. The results showed that majority of the population are not working for a wage, commission or salary, which also pointed to the gravity of unemployment situation in the area of study. Most of the working population (both male and female) are lowest earners (R ≤ 10,000), and this also cuts across all age-group categories. Majority of working population have no formal education, are drop out, or have less than grade-12 certificate, and very few working populations with higher education status were found in the moderate and relatively high earnings categories. While many of the working population are engaged in the informal sector, those in the formal sector are in the lowest earners group. Compared to any other race, the Black African group constituted the majority of non-wage earners, and most in this group were found in the lowest earners group. Some of the working population who were beneficiaries of social grants and medical aids scheme were found in the lowest, low, and moderate earnings categories. The findings significantly isolated the earnings-effect of age, marital status, gender, race, education, geographic indicators, employment sector, and index of health conditions and disabilities. The study recommends interventions addressing racial, gender, and geographic wage gaps, while also emphasizing the importance of equitable access to education, health infrastructure, and skills development.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

In the human and economic development context, this study examines the relationship between human capital, life expectancy, labor force participation rate, and education level in Indonesia, Malaysia, and Thailand. The World Bank’s 2001–2021 data are examined using a panel vector autoregressive model. The findings demonstrate the substantial influence of health expenditure from the prior period on present health expenditure. Though not significantly different, life expectancy and education levels from earlier periods also impact present health spending. A slight positive correlation exists between prior labor force involvement and present healthcare costs. An increase in current health expenditure supports an increase in life expectancy. Health expenditure in the previous period had a significant positive effect on education, although insignificant. Life expectancy in the previous period harms current education but is also insignificant. Education in the previous period significantly positively affects current education, indicating a sustained impact of education investment. Labor force participation in the previous period also positively affected education, although not significantly. The prior period’s health spending, life expectancy, and educational attainment impact the current labor force participation rate. The length of life has a significant favorable impact on entering the labor sector. Currently being in the job field has a good correlation with prior education as well. These findings support that higher education levels lead to higher labor force participation rates. Life expectancy, health care costs, education level, and prior work experience all influence current life expectancy. While prior life expectancy significantly influences current life expectancy, health expenditures have a negligible negative impact. Prior education positively impacts life expectancy but negatively impacts prior labor force engagement. These results reject the hypothesis that increasing life expectancy causes current health expenditure to increase.

This study examines the challenges and needs faced by non-profit organisations (NPOs) in Colombia regarding the adopting of the International Financial Reporting Standards (IFRS) for small and medium enterprises (SMEs), particularly focusing on sections 3 and 4. Employing a mixed-method approach, the research combines qualitative and quantitative methods. Surveys were conducted with Colombia NPOs, official documents were analysed, and comparative case studies were performed. In-depth interviews and participant observation were also utilised to gain a comprehensive understanding of the obstacles and current practices within the Colombian context. The findings reveal that NPOs in Colombia encounter significant difficulties in adopting IFRS due to the complexity of the standards, lack of specialised resources, and the need for specific training. Internal challenges such as deficiencies in staff qualifications and training, resistance to change, and technological limitations were identified. Externally, ambiguities in the legal framework and donor requirements were highlighted. The case study illustrated that, while there are similarities between IFRS for SMEs and the IFR4NPO project, specific adaptations are essential to address the unique needs of NPOs. This research underscores the necessity of developing additional guidelines or modifying existing ones to enhance the interpretation and application of IFRS in Colombia NPOs. It is recommended to implement proactive strategies based on education and legislative reform to improve the transparency and comparability of financial information. Adopting a more tailored and supported accounting framework will facilitate a more relevant and sustainable implementation, benefiting Colombian NPOs in their resource management and accountability efforts.

The issue of policy changes to support teacher professional development is an important factor shaping the career trajectory, efficacy, and ultimately the success of Junior Reserve Officer Training Corps (JROTC) instructors and the performance of the secondary students they serve and whose lives they affect. Although a rich body of research associated with policies regarding teacher preparation and professional development exists, a more closely related area of research focused specifically on the policies regarding preparation and professional development of JROTC instructors is limited. This lack of research presents a unique opportunity to explore the experiences of JROTC instructors and their perspectives on policies affecting teacher preparation and professional development. This qualitative exploratory single-case study can help to advance understanding of the complexities and nuances of teacher preparation and professional development policies supporting the JROTC instructors serving in high schools across the United States and overseas. One-on-one interviews with 14 JROTC personnel who had completed required teacher preparation requirements and professional development initiatives were conducted. Data analysis revealed 11 themes. Recommendations for improving policies concerning JROTC instructor preparation and professional development, including placing greater emphasis on the unique requirements, as well as suggestions for future research, are provided.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.