Masters Degrees (Economics)

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Browsing Masters Degrees (Economics) by browse.metadata.advisor "Boshoff, Willem"

Now showing 1 - 3 of 3
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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.
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    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.
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    Simulated horizontal mergers in vertically related markets
    (Stellenbosch : Stellenbosch University, 2022-04) Marais, Wihan; Boshoff, Willem; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY: A global trend of increasing retail concentration and a heightened concern about buyer power demand more sophisticated and flexible merger screening tools. Parametrisable merger simulation models that suitably account for the effects of bargaining competition may pose a solution. Thus, this dissertation examines the predicted effects o f retail consolidation in a vertically related market using the merger simulation tool developed by Tschantz and Froeb (2019). With the flat logit nested demand function for differentiated products of Boshoff e t a l. (2020), the predicted retail merger effects of five different bargaining and non-bargaining models for a 1 × 2 industry are evaluated. The primary contribution of this study is its unique application of Nashin-Shapley (NiS) bargains in the study of retail consolidation. It is novel in so far it compares the inherent implications of Nash-in-Nash (NiN) and NiS bargains for the appraisal of retail mergers. Pre-merger competitive outcomes with NiN predetermine a finding o f an anticompetitive merger. Contrarily, predictions with NiS as its starting point do not indicate that a merger would cause consumer harm, and therefore, would not be prohibited. In addition, the results suggest that retail consolidation consistently poses a viable option through which retailers can improve their profitability and bargaining positions. However, merger incentives are moderated by the competitiveness of an outside market.