Masters Degrees (Economics)
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- ItemSchool-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.
- ItemOvercoming 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.
- ItemCorrelating 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.
- ItemA 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.
- ItemFiscal instruments and the elderly population in South Africa : a distributional analysis(Stellenbosch : Stellenbosch University, 2022-04) Huffer, Calvin; Jansen, Ada; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.ENGLISH SUMMARY: Saving an adequate amount of money to finance retirement spending is a growing issue that affects the elderly. In response to the incomplete retirement savings markets that the elderly face, national governments employ fiscal instruments in an attempt to provide social security to the economically vulnerable elderly population. In South Africa, the government utilizes four primary fiscal instruments to address the lack of retirement savings among the elderly: the old age grant, a higher tax rebate for the elderly, an additional medical expenses tax credit for out-of-pocket expenses and medical aid fees of those 65 years and older, and preferential tax treatment of pension contributions. Combined, these four fiscal instruments form the social security net for the elderly in South Africa, although concerns of some individuals still slipping through the net remain. Therefore, an analysis into the effectiveness of these fiscal instruments in addressing incomplete markets and issues related to intended beneficiaries is undertaken to evaluate social security provision for all segments of the South African elderly population. The fiscal instruments are assessed both individually and in combination by means of economic theory and microsimulation using the South African tax-benefit Microsimulation Model (SAMOD). Microsimulation using the SAMOD allowed for a comparison of current fiscal instruments against alternative scenarios to assess the relative costs and benefits of policy reforms in terms of government revenue, poverty, and inequality. The underpinning household survey data of the SAMOD allows for the static economic implications of potential reforms to be evaluated and inform the effectiveness of the current social security system. The South African context and existing empirical evidence are used to determine whether policy reforms would be socially beneficial to the elderly. This investigation finds that while the fiscal instruments employed by the South African government are reasonably established for those in low- and higher-income groups among the elderly population, those in the middle-income groups may fall through the gaps of the social security system. Reform of the existing fiscal instruments to include these individuals would create more comprehensive social security for the elderly in South Africa and it can be accomplished with a relatively small impact on government expenditure. More extensive reform could be accomplished with greater fiscal expenditure on the elderly, but even within the confines of the budgetary constraints, modest reform of the fiscal instruments affecting the elderly can have positive societal benefits.