Masters Degrees (Economics)

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 85
  • Item
    Assessing the economic impact of road traffic injuries on privately insured healthcare recipients in South Africa during the covid-19 pandemic
    (Stellenbosch : Stellenbosch University, 2024-03) Mboko, Lewis Tendai; Sophia, du Plessis; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: Background: Road Traffic Injuries (RTIs) are a global public health problem, with around 1.3 million deaths annually. According to the World Health Organisation (2020), road traffic injuries are the 10th leading cause of death in upper-middle-income countries and constitute one of the five major diseases and conditions with the highest mortality and morbidity in South Africa (Mabuza, Titus and Adeniji, 2020). Cost of Injury (COI) studies are essential to estimate the burden of injuries and are good guides for policymaking, priority setting, and public health management. However, a few COI studies have been conducted in low- and middle-income countries, even though more than 85% of injuries and death happen in the developing world (Wijnen, 2021). South Africa is not an exception to the lack of sufficient studies to assess the socioeconomic impact of road traffic crashes. The lack of studies makes it difficult to assess the cost-effectiveness of prevention methods, resulting in a lack of comprehension of the problem's scope. Aim: This study aimed to comprehensively assess the economic burden of road traffic injuries in South Africa by incorporating both direct medical costs and indirect costs from a healthcare system perspective in the private sector. Methods: Employing a retrospective Cost of Illness (COI) approach, the study evaluated the direct medical costs of road traffic injuries among BestMed-insured patients involved in accidents during 2020 and 2021. Furthermore, Indirect costs, including productivity loss and long-term healthcare expenses, were estimated using data from previous studies. Detailed claims information was utilized to track patient treatment costs specifically related to the respective accidents. Results: The average medical direct cost for treating a single road traffic injury in the study cohort was R58 964 ($3211), equivalent to 588% of South Africa's health expenditure per capita and 50.7% of the average Gross Domestic Product (GDP) per capita. Incorporating indirect costs substantially increased the economic burden of RTIs. The average indirect cost per crash stood at R 196,699 ($11,015). Factors such as gender, comorbidities, complications, hospital stay duration, and Major Diagnostic Categories (MDCs) significantly influenced injury costs. Conclusion: South Africa's average cost of treating road traffic injuries is significantly higher than the country's healthcare expenditure per capita. Cost of Injury analyses, stratification of costs, and employing regression models with accurate cost data provides a better understanding of the overall economic burden of road traffic injuries in South Africa. Placing a financial value on the tangible and intangible losses attributed to road traffic crashes makes the need for immediate and far-reaching intervention clear to policymakers and decision-makers.
  • Item
    School-based mechanisms of learner self-efficacy, engagement value and achievement : a structural equation analysis of grade 9 mathematics performance in South Africa
    (Stellenbosch : Stellenbosch University, 2023-12) Takalani, Mukovhe Glen; Shepherd, Debra; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: Since 2006, a revision to the basic education curriculum has made mathematics compulsory for all South African students during the Further Education and Training (FET) phase. This sewed to both rectify historical inadequacies in mathematical literacy, as well as meet demands of contemporary' economy and the Fourth Industrial Revolution (41R). Cross-time trends in the Trends in Mathematics and Sciences Study (TIMSS) have indicated substantial improvements in mathematics achievement of Grade 9 South African learners between 1995 and 2019. Nevertheless, South African students, on average, continue to lag internationally, and there exist significant gaps in mathematical proficiency across socioeconomics groups, as well as by gender (albeit to a lesser degree The empirical analysis presented in this thesis aimed to examine the complex relationship between leaner academic self-efficacy, engagement, and expectancy value, and the association of these with mathematics achievement. The Situated Expectancy-Value Theory (S-EVT) of Eccles and Wigfield (2020) contends that a learner' s motivational and competency beliefs dynamically evolve with each learning situation. Central to this evolution are the experiences and perceptions of the behaviour of key socializers, i.e., teachers and peers, and sociocultural attitudes such as gender stereotyping. The TIMSS data for South African Grade 9 learners collected in 2019 was used together with Structural Equation Modelling (SEM) using Maximum Likelihood Missing Value (MLMV) estimation. SEM analyses performed by school socio-economic classification and gender aimed to emphasize the role of perceptions of socializer behaviour, affective reactions, self-schemas, and task values on mathematics achievement. The findings point towards successful outcomes in mathematics to be nurtured within an emotional ecosystem where students through an instilled sense of competence and interest forge a genuine bond with the subject, leading to enhanced mathematical proficiency. However, this account is not uniform. but entwined with gender- and class-based nuances. While the social cognitive processes of both boys and girls were influenced by perceptions of teacher social support and instructive engagement (TSSE), the effect sizes estimated for boys were more pronounced. This supports existing research (e.g., Watt et al., 2019) that boys, more than girls, necessitate an augmented level of effort, interaction, and support from their educators to stimulate their interest in and utility value from mathematics. This is, perhaps, because it serves as a countervailing force against prevailing negative expectations. For girls, TSSE emerged as a significant determinant of interest in mathematics, a subject traditionally perceived as aligning with masculine attributes. This "effect" emerged predominantly through the mechanism of mathematics self-efficacy (MSE), underscoring the important role that teachers can play in fostering girls' confidence in their mathematical capabilities. In poorer school contexts, however, the MSE of both girls and boys were negatively influenced by peer relations. Finally, differential paths from MSE to mathematics achievement were found for boys and girls: For girls and particularly those in more affluent schools the total effect of MSE on performance operated predominantly through intrinsic task value, whereas for boys in less-affluent settings, it operated through utility task value. These findings suggest a deep rootedness of socioeconomic context in goal orientations.
  • Item
    Overcoming measurement error in household consumption data : using novel data to characterise consumption
    (Stellenbosch : Stellenbosch University, 2023-12) Vivier, Chanté; Von Fintel, Dieter; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: The quality of household consumption data is declining, despite their centrality in answering important economic questions. As a result, a number of alternative approaches to gathering household consumption and expenditure data have been proposed in an attempt to address the declining quality of the data. The purpose of this paper is to contribute to the literature on the measurement and characterisation of household consumption through the use of novel data in the form of household municipal solid waste and retail store receipts. This paper theorises that household waste and store receipts can be used to construct more direct measures of consumption and expenditure, respectively, and in so doing address the measurement errors to which traditional sources of household consumption data are prone. On the premise that these alternative sources of data are not prone (or at least not as prone) to the same measurement errors characteristic of traditional sources of consumption data, it uses these measures to characterise household consumption behaviour of households in two towns in South Africa. The paper finds that, used together, the store receipts and household waste tell a congruent story. The results from this paper suggest that there is potential for the use of store receipts and household waste as a measure of expenditure and consumption, respectively.
  • Item
    Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
    (Stellenbosch : Stellenbosch University, 2023-03) Rees, Pablo; van Lill, Dawie; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: The literature relating textual to stock market data is deep, but the relationship between speeches given by political figures and stock markets is relatively undefined. This research begins to rectify this by exploring the relationship between U.S. presidential speeches and daily price movements in the S&P 500 index. It was possible to explore this relationship by using natural language processing techniques, econometric time-series analysis, and machine learning models. It was found that models including presidential speech data can achieve prediction accuracy of about 60% over an S&P 500 index price movement proxy. This is an increase of about 0.3% (0.599 vs 0.601) over the models that did not include the presidential speech data (without losing ground in either recall or precision). Notably, this result was drawn from 71 years of data at a daily resolution. Thus, it is concluded that presidential speeches hold predictive power over stock market movements and that this relationship can be used to improve the power of predictive models.
  • Item
    A comparison between existing mortality risk algorithms and machine learning techniques
    (Stellenbosch : Stellenbosch University, 2022-12) Scholtz, Jenny; Burger, Rulof; Retief, Riani; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: This thesis assesses the feasibility and benefits of using the patient data of a large private South African hospital group to estimate a model of mortality risk using flexible machine learning techniques. Specifically, I investigate whether such a model would have been able to outperform a commonly used medical scoring system, SAPS 3, in predicting mortality during the second half of the Covid-19 pandemic. A LightGBM machine learning model is shown to be much more accurate in predicting mortality (76.15% accuracy, compared to 56.58% for SAPS 3) for the Covid-19 positive sample. Roughly half of this gain in predictive accuracy is obtained from using the most recent and relevant data to train the model, while the remaining lift is attributable to allowing the model to find patient symptoms and attributes that are measured but ignored by SAPS 3. Interestingly, the flexible functional form of the machine learning models, which allow the predictors to affect mortality through non-linearities and interactions, has a negligible effect on predictive accuracy. The same method is also found to produce more accurate forecasts for patients who tested negative for Covid-19, but this improvement is smaller than for Covid-19 positive sample. The results of this thesis illustrate that machine learning methods are valuable tools to predict patient outcomes, particularly when there are unexpected shifts in the relationship between patient features and patient outcomes. Large hospital groups can obtain more accurate forecasts from a dynamic scoring system which is frequently frequently retrained on their own patient data.