Search Results (33)

In today’s fast-paced and evolving job market, salary continues to play a critical role in career decision-making. The ability to accurately classify job titles and predict corresponding salary ranges is increasingly vital for organizations seeking to attract and retain top talent. This paper proposes a novel approach, the Hybrid Bayesian Model (HBM), which combines Bayesian classification with advanced regression techniques to jointly address job title identification and salary prediction. HBM is designed to capture the inherent complexity and variability of real-world job market data. The model was evaluated against established machine learning (ML) algorithms, including Random Forests (RF), Support Vector Machines (SVM), Decision Trees (DT), and multinomial naïve Bayes classifiers. Experimental results show that HBM outperforms these benchmarks, achieving 99.80% accuracy, 99.85% precision, 100% recall, and an F1 score of 98.8%. These findings highlight the potential of hybrid ML frameworks to improve labor market analytics and support data-driven decision-making in global recruitment strategies. Consequently, the suggested HBM algorithm provides high accuracy and handles the dual tasks of job title classification and salary estimation in a symmetric way. It does this by learning from class structures and mirrored decision limits in feature space. Full article

Soccer’s global popularity as the world’s favorite sport is driven by many factors, with high player salaries being one of the key reasons behind its appeal. These salaries not only reflect on-field performance, but also capture a broader evaluation of player value. Despite the increasing use of performance data in sports analytics, a critical gap remains in establishing fair compensation models that comprehensively account for both quantifiable and intangible contributions. To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. This research focuses on reducing bias and improving transparency in salary decisions through a systematic, data-driven approach. Utilizing a dataset spanning the 2016–2022 seasons, we apply both traditional and automated ML frameworks to uncover the most influential factors in salary determination. The results indicate a nearly 17% improvement in R² and about a 30% reduction in MAE after incorporating the newly constructed features and methods, demonstrating a significant enhancement in model performance. Gradient Boosting demonstrates superior effectiveness, revealing a group of significantly underestimated and overestimated players, and showcasing the model’s proficiency in detecting valuation discrepancies. Full article

Graduate salaries are a significant concern for graduates, employers, and policymakers, as various factors influence them. This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statistical Agency) and integrating advanced machine learning (ML) explanatory techniques with statistical analytical methodologies. By employing multi-stage analyses alongside machine learning models such as decision trees, random forests and the explainability with SHAP stands for (Shapley Additive exPanations), this study investigates the influence of 21 socioeconomic and demographic variables on graduate salary outcomes. Key variables, including institutional reputation, age at graduation, socioeconomic classification, job qualification requirements, and domicile, emerged as critical determinants, with institutional reputation proving the most significant. Among ML methods, the decision tree achieved a standout with the highest accuracy through rigorous optimisation techniques, including oversampling and undersampling. SHAP highlighted the top 12 influential variables, providing actionable insights into the interplay between individual and systemic factors. Furthermore, the statistical analysis using ANOVA (Analysis of Variance) validated the significance of these variables, revealing intricate interactions that shape graduate salary dynamics. Additionally, domain experts’ opinions are also analysed to authenticate the findings. This research makes a unique contribution by combining qualitative contextual analysis with quantitative methodologies, machine learning explainability and domain experts’ views on addressing gaps in the existing identification of graduate salary predicting components. Additionally, the findings inform policy and educational interventions to reduce wage inequalities and promote equitable career opportunities. Despite limitations, such as the UK-specific dataset and the focus on socioeconomic and demographic variables, this study lays a robust foundation for future research in predictive modelling and graduate outcomes. Full article

The sustainability and adequacy of pension systems are central to public policy debates in aging societies. This paper introduces a novel microsimulation model with probabilistic behavior to assess these dual challenges in the Spanish pension system. The model employs a mixed-projection method, integrating a macro approach—using economic and demographic aggregates from official sources such as the Spanish Statistics Office (INE) and Eurostat—with a micro approach based on the Continuous Sample of Working Lives (MCVL) dataset from Spanish Social Security. This framework enables individual-level projections of key labor market variables, including work time, salary, and initial pensions, under diverse reform scenarios. The results demonstrate the model’s ability to predict initial pensions with high accuracy, providing detailed insights into adequacy by age, gender, and income levels, as well as distributional measures such as density functions and quantiles. Sustainability findings indicate that pension expenditures are projected to stabilize at 13.9% of Gross Domestic Product (GDP) by 2050. The proposed model provides a robust and versatile tool for policymakers, offering a comprehensive evaluation of the long-term impacts of pension reforms on both system sustainability and individual adequacy. Full article

As early career researchers, postdocs play an irreplaceable and crucial role in scientific research, especially in highly competitive fields. Given the importance of the postdoc community, it is essential to explore their engagement in the academic labor market. Employment in academic departments should be the ideal career choice for postdocs, but this is not always the case. In recent years, an increasing number of postdocs have chosen to leave the ivory tower, which is often the result of a dynamic integration of the work environment and individual cognition. This study is based on the public data from the “Nature 2023 Postdoc Survey” and empirically analyzes the predictive relationship between different dimensions of environmental perception and postdoc academic career choices, as well as whether there are differences in predictive relationships across various fields. Difference analysis suggested that males and those working in their native country tend to have greater satisfaction in their environment perception. Correlation analysis revealed that postdocs’ environmental perception is significantly and positively correlated with academic career choices. Regression results indicated that institutional environment, organizational environment, living environment, and support environment can all predict postdoc academic career choices, with significant disciplinary differences in these predictive effects. It is recommended that mental health, salary and benefits, job security, and professional training be focused on to improve the working environment for early career researchers. Additionally, it is necessary to increase inclusive support for vulnerable postdoc groups and enhance their expectations for academic careers. Full article

The literature associating the spread of SARS-CoV-2 with the healthcare-related, geographical, and demographic characteristics of the territory is inconclusive and contrasting. We studied these relationships during winter 2021/2022 in South Tyrol, a multicultural Italian alpine province, performing an ecological study based on the 20 districts of the area. Data about incidence, hospitalization, and death between November 2021 and February 2022 were collected and associated to territorial variables via bivariate analyses and multivariate regressions. Both exposure variables and outcomes varied widely among districts. Incidence was found to be mainly predicted by vaccination coverage (negative correlation). Mortality and ICU admission rates partially followed this distribution, while the case fatality rate was inversely correlated to average salary, and hospital admission rates increased where hospitals capacity was higher, and from the southern to the northern border of the province. These findings, besides confirming the efficacy of vaccination in preventing both new and severe SARS-CoV-2 cases, highlight that several geographical and socio-demographic variables can be related to disease epidemiology. Remote areas with wage gaps and lower access to care suffered most from the pandemic. Our findings, therefore, underly the existence of health inequity issues that need to be targeted by implementing specifically tailored public health interventions. Full article

This entry provides a review of economic models of professional sports leagues with and without revenue sharing. These include models that assume profit-maximizing and win-maximizing (sportsmen) club owners. Both approaches predict that revenue sharing will reduce the demand for player talent, depress player salaries, and transfer revenue from large-market to small-market clubs, but they differ on league parity effects. Empirical work has been sparse due to financial data limitations and has not yielded definitive results on the parity issue. Despite the growing awareness of sports economics in the sports industry, the lack of consensus from theoretical models has resulted in sports leagues searching for an optimal revenue sharing policy. The difficulty in providing consistent policy prescriptions in models that incorporate revenue sharing, salary caps, and other league policies has made economic modeling of sports leagues very difficult and complex. While revenue sharing remains an interesting theoretical modeling issue, it has not bridged the gap to real-world league policies. Full article

Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze how capping contributed salaries can help to mitigate the effects of population aging on the retirement system. Under generous Pay-As-You-Go schemes, promised pension payouts far exceed contributions. Thus, restricting contributions is expected to reduce the burden of future benefits by accepting lower contributions today, while directing public subsidies to low-income individuals. We simulate the future evolution of the financial balance of Algeria’s retirement system under various contributable salary caps versus various scenarios of environmental evolution and potential parametric reform actions. The results demonstrated that a 40% cap, along with major parametric reforms and an ideal environment, would help achieve a cumulatively balanced system in the long run. Full article

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units. Full article

It has been documented that there is a positive relationship between a worker’s subjective well-being and productivity, and individuals who are happy in their work have a better attitude when performing activities: happier employees are more productive. Turnover intention, on the other hand, may arise from various factors rather than merely the need to increase a salary, as the traditional economic theory states. The fact that the work performed does not contribute to the worker’s life purpose, that there might be a bad relationship with colleagues, or else might play a role in the search for a new job. This study aims to show the relevance of meaningful work in happiness at work and turnover intention. Data from 937 professionals, in 2019, in Mexico were analyzed. Regression analyses were used to assess the impact of meaningful work on happiness at work and turnover intention. Results show that meaningful work, feeling appreciated by coworkers, and enjoyment of daily tasks significantly predict happiness at work. A logit model showed that having a job that contributes to people’s life purpose, feeling appreciated, and enjoyment of daily tasks reduces turnover intention. The main contribution of the study is to identify the importance of elements of purpose and meaning in the work context, contributing to economic theory. Limitations include the use of single items from a more extensive survey, which might diminish the validity and reliability of the constructs under scrutiny. Future directions point towards the need for more robust indicators of the variables of interest, but the findings emphasize the importance of research focused on the meaning workers attribute to their own work and the effects this attribution might have on their own wellbeing, organizational results, and productivity, including a return of investment (ROI) indicators. Full article

This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset contains information on 17,898 graduates and approximately 148 features. Three machine learning algorithms, namely, decision trees, random forest, and gradient boosting, were used for data analysis. These three machine learning models were compared with ordinal regression. The results indicate that gradient boosting is the best predictive model, which is 6% higher than the ordinal regression accuracy. The SHapley Additive exPlanations (SHAP), a novel methodology to extract the significant features of different machine learning algorithms, was then used to extract the most important features of the gradient boosting model. Current salary is the most important feature in predicting job levels. Interestingly, graduates who realized the importance of communication skills and teamwork to be good leaders also had higher job positions. Finally, general relevant features to predict job levels include the number of people directly in charge, company size, seniority, and satisfaction with income. Full article

Background: Students at universities increasingly struggle with mental health issues such as anxiety, depression, and stress. The present prevalence of these diseases may arise in the event of a crisis such as the coronavirus disease 2019 (COVID-19) pandemic. Aim: To estimate the level of anxiety, depression, and stress in female university nursing students, and to identify predictors for students’ anxiety, depression, and stress during the COVID-19 pandemic. Methods: An online cross-sectional descriptive study was conducted using a convenient sample of 115 female university nursing students. The Depression Anxiety Stress Scale (DASS-21) questionnaire was used to assess symptoms of anxiety, depression, and stress. Multivariate linear regression was used to identify predictors of anxiety, depression, and stress. Results: Stress, anxiety, and depression had prevalence rates of 23.7%, 18.5%, and 34.6%, respectively. Significant anxiety predictors included family support, family salary, being diagnosed with chronic illness, and being exposed to patients with COVID-19. Significant correlations were found between family support, family salary, family history of mental illness, and fear of being infected with COVID-19 and depression in female university nursing students. Students’ levels of stress were predicted by family support. Conclusion: The level of anxiety, depression, and stress among female university nursing students was determined to be moderate. It is advised that university nursing students receive interventions that support their mental health. Full article

A holistic occupational and economy-wide framework for salary prediction is developed and tested using statistical machine learning (ML). Predictive models are developed based on occupational features and organizational characteristics. Five different supervised ML algorithms are trained using survey data from the Saudi Arabian labor market to estimate mean annual salary across economic activities and major occupational groups. In predicting the mean salary over economic activities, the Bayesian Gaussian process regression ML showed a marked improvement in $R^2$ over multiple linear regression (from 0.50 to 0.98). Moreover, lower error levels were obtained: root-mean-square error was reduced by 80% and mean absolute error was reduced by almost 90% compared to multiple linear regression. However, the salary prediction over major occupational groups resulted in artificial neural networks performing the best in terms of both $R^2$, with an improvement from 0.62 in multiple linear regression to 0.94 and errors were reduced by approximately 60%. The proposed framework can help estimate annual salary levels across different types of economic activities and organization sizes, as well as different occupations. Full article

In the context of biodiversity crisis, targeted efforts are required to accelerate the discovery and description of the still-unknown species. In the present study, we collected data on current knowledge of plant richness in South Africa and used a statistical modeling technique to predict what might still be missing in the country. We found that we might be missing 1400–1575 plant species, and it might take 40–45 years to identify and describe these species aided by 64–315 taxonomists. Surveyed taxonomists spent USD 95,559, on average, to describe one species. At this rate, USD 150,506,142 would be required to describe the 1575 species (modeling) or USD 133,783,237 for the 1400 remaining species (expert opinion). However, these estimates do not correspond to what is specifically required for only species description but does integrate connected activities, e.g., running cost, bursary, salaries, grants, etc. Furthermore, these estimates do not account for the possibility of taxonomic revision, which, on its own, needs to be funded, nor do they account for molecular laboratory requirement. Nevertheless, if we consider that 15% of the predicted funds are solely spent on taxonomic activities, we would need ~USD 14,334 on one species. Overall, our study provides figures that can inform attempts to fuel efforts toward a comprehensive assessment of the unique South Africa’s biodiversity. Full article

Given that global amounts of waste are growing rapidly, it is extremely important to determine what amount of waste will be generated in the near future. Accurate waste forecasting is also important for planning and designing a sustainable municipal solid waste (MSW) management system. For that reason, there is a need to build a model to predict the amount of MSW generated in the near future. Based on previous research, artificial neural networks (ANN) show better results in predicting waste generation compared to other mathematical models. In this research, an ANN model using the iterative algorithm Broyden–Fletcher–Goldfarb–Shanno (BFGS) for the prediction of MSW fractions, based on the socio-demographic characteristics, economic and industrial data obtained in Croatia and summarized data of the member states of EU (EU-27 from 2020), showed good predictive capabilities. The coefficient of determination during the training cycle for the output variables; household and similar waste (HHS), paper and cardboard waste (PCW), wood waste (WW), textile waste (TW), plastic waste (PW) and glass waste (GW) were 0.993; 0.997; 0.999; 0.997; 0.998; and 0.998, respectively, while reduced chi-square, mean bias error, root mean square error, mean percentage error, average absolute relative deviation and sum of squared errors were found low. In this paper, Yoon′s method of interpretation shows the relationships between socio-demographic data and the amount of generated waste. The results indicate that the lowest level of education shows a negative impact on observed waste-types calculations, with a relative impact between −9.889 and −4.467%. The most pronounced positive impact on the calculation of HHS, PCW, WW, TW, PW and GW was observed for year variable, gross domestic product, exports of goods and services, imports of goods and services, wages and salaries, secondary income, arrivals in collective accommodation establishments, overnight stays in collective accommodation establishments and exports of petroleum and petroleum products to partner countries, with a relative influence of 4.063–7.028; 2828–4851; 5240–6197; 5.308–6.341; 4290–4810; 4533–5805; and 4.345–4.493, respectively. The obtained results indicate that the amount of HHS waste at the EU-27 level in 2025 will decrease by approximately 18% compared to the data from 2018. The quantities of other observed recyclable types of waste will increase by 34% for PCW, 310% for WW, 40% for TW, 276% for PW and about 67% for GW. The amount of waste generated provides the basic information needed to plan, operate and optimize the waste management system. It could also help in the transition to an environmentally friendly and economically profitable circular economy. The model created in this research could also help with the system of separate waste collection, which would lead to more efficient recycling and the achievement of the set goals for recycling 55% of municipal waste by 2025. Full article