Masters Degrees (Economics)

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https://scholar.sun.ac.za/handle/10019.1/713

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Assessing cartel detection and damages in simulated markets : a comparative study of econometric and machine learning approaches
(Stellenbosch : Stellenbosch University, 2024-03) Visser, Amy Sharon; Boshoff, Willem; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
ENGLISH SUMMARY: Collusion among firms, with the intent to artificially elevate prices, has far-reaching implications for market competition and consumer welfare. This thesis explores the detection of structural breaks in simulated price data under collusion, and their subsequent impact on damage estimation. This contribution is significant in the field of competition economics as it allows for consideration of the implications for econometric methods aimed at identifying and measuring collusive effects in the age of machine learning alternatives. A combination of econometric and machine learning approaches, including Lasso regression, random forest regression and classification, logistic regression, and Bai-Perron structural break testing are rigorously examined against four distinct data generating processes simulated to mimic the behaviours of cartels observed in the market. These include a deterministic switch data generating process, a recurrent switch data generating process, a phased switch data generating process, and a Markov-switching data generating process. The study reveals that the Lasso model consistently outperforms the other methods in estimating structural breaks, demonstrating superior performance in identifying cartel and competitive pricing behaviours across the different linear data generating processes. Conversely, the Bai-Perron test exhibits the poorest performance, particularly in Phase and Markov-switching transitions, highlighting its limitations in capturing nuanced structural changes. Furthermore, damage estimation was performed using dummy variables generated by each of the models. All of the empirical models perform relatively well in capturing damages, with the exception of the Bai-Perron model when applied to the phase and Markov-switching data generating processes, further emphasising its limited utility in detecting nuanced switching mechanisms in pricing behaviour. To enhance the analysis, damage estimation was alternatively conducted by predicting movements in the price variable for the Lasso and random forest models. These modifications revealed slight discrepancies in damage predictions, with the Lasso model overpredicting and the random forest model underpredicting damages. Nevertheless, both models remain highly accurate in capturing the economic impact of structural changes in competitive pricing. This research contributes to the field of competition economics by providing a comprehensive analysis of structural break detection and damage estimation methodologies, ultimately demonstrating the practical advantages of the Lasso regression model when applied to linear pricing models. These findings offer valuable insights for policymakers and analysts seeking to better understand and address changes in competitive market dynamics.
The predictive power of public interest concerns relative to competition concerns in South African merger control
(Stellenbosch : Stellenbosch University, 2024-03) Morris, Ronan Mitchell; Boshoff, Willem; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
ENGLISH SUMMARY: This thesis seeks to ascertain the relative importance of public interest concerns and competition concerns in predicting South African merger adjudication decisions. In particular, the thesis empirically tests the hypothesis that public interest concerns have become more important in driving merger intervention than competition concerns. While earlier research suggests a statistically significant relationship between public interest concerns and the conditional approval of mergers, the relative importance of public interest and competition concerns as drivers of the merger adjudication process in South Africa has not received empirical attention. Such an empirical study is timeous in the light of policy attempts to provide clarity on the role of public interest concerns, including through the publication of guidelines. The thesis also posits two secondary, and related, hypotheses. First, that the South African Competition Commission has become increasingly interventionist since the 2018 amendments to the Competition Act, ceteris paribus. Second, that all public interest concerns are not treated equally, i.e. that public interest concerns vary in their impact on merger intervention. The thesis relies on publicly available merger data released by the South African Competition Commission in 2022. It includes over three thousand unique merger cases, and covers a wide array of variables that influence merger decisions. The methodology in this paper is derived from the machine learning literature. The specific algorithm employed is that of a random forest model, which produces a variable importance plot and allows for partial probability analysis. These measures provide the empirical basis for evaluation the stated hypotheses. The thesis concludes that public interest concerns are more important for predicting merger adjudication decisions than individual competition concerns. It is noteworthy that, on average, small business concerns, BBBEE concerns, and concerns around the ability of national firms to compete in international markets all raise the probability of merger intervention by more than raising three separate competition concerns. It is further shown than the Commission has become increasingly interventionist after the 2018 amendments to the Act, ceteris paribus. Finally, it is concluded that public interest concerns should not be treated as a uniform group when estimating their effects on the adjudication of a merger, as they have distinct effects on the probability of intervention.
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.
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.
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.

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