AUT School of Economics
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The AUT School of Economics has an established record and an on-going commitment to excellent research, high-quality supervision, and community and professional engagement. Members of the School sit on editorial boards and serve as referees for professional journals. The school has particular research strength in; Micro and macroeconomics, Econometrics, Industrial organisation, International trade and finance, Natural resource and environmental economics, Labour economics, Economic development, Health economics, and Public policy.
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Browsing AUT School of Economics by Author "Greyling, T"
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- ItemBig Data and Happiness(Global Labor Organization, 2020-09-02) Rossouw, S; Greyling, TThe pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on their journey. However, in an everchanging world, one needs high-frequency data instead of data released with significant time-lags. High-frequency data, which stems from Big Data, allows policymakers access to virtually real-time information that can assist in effective decision-making to increase the quality of life for all. Additionally, Big Data collected from, for example, social media platforms give researchers unprecedented insight into human behaviour, allowing significant future predictive powers.
- ItemThe Good, the Bad and the Ugly of Lockdowns During Covid-19(Public Library of Science (PLoS), 2021-01-22) Rossouw, S; Greyling, T; Adhikari, TAmidst the rapid global spread of Covid-19, many governments enforced country-wide lockdowns, with likely severe well-being consequences. In this regard, South Africa is an extreme case suffering from low levels of well-being, but at the same time enforcing very strict lockdown regulations. In this study, we analyse the causal effect of a lockdown and consequently, the determinants of happiness during the aforementioned. A difference-in-difference approach is used to make causal inferences on the lockdown effect on happiness, and an OLS estimation investigates the determinants of happiness after lockdown. The results show that the lockdown had a significant and negative impact on happiness. In analysing the determinants of happiness after lockdown, we found that stay-at-home orders have positively impacted happiness during this period. On the other hand, other lockdown regulations such as a ban on alcohol sales, a fear of becoming unemployed and a greater reliance on social media have negative effects, culminating in a net loss in happiness. Interestingly, Covid-19, proxied by new deaths per day, had an inverted U-shape relationship with happiness. Seemingly people were, at the onset of Covid-19 positive and optimistic about the low fatality rates and the high recovery rates. However, as the pandemic progressed, they became more concerned, and this relationship changed and became negative, with peoples' happiness decreasing as the number of new deaths increased.
- ItemHappiness-lost: Did Governments Make the Right Decisions to Combat Covid-19?(Global Labor Organization, 2020-06-07) Rossouw, S; Greyling, T; Tamanna, AAmidst the rapid global spread of Covid-19, many governments enforced country-wide lockdowns, with likely severe well-being consequences. The actions by governments triggered a debate on whether the well-being and economic costs of a lockdown surpass the benefits perceived from a lower infection rate. In this regard, South Africa is an extreme case: enforcing very stringent lockdown regulations, while amid an economic crisis. We analyse the impact of both Covid-19 and the lockdown on happiness. We use the Gross National Happiness Index to compare the determinants of happiness before and after the Covid-19 lockdown regulations. Further, we estimate the likelihood of happiness levels in 2020, reaching the average levels in 2019 using two models; one predicting the likelihood after the lockdown was enforced and the other if no lockdown regulations were in place. The results shed light on happiness outcomes in a scenario of lockdown versus no lockdown.
- ItemMarkov Switching Models for Happiness During a Pandemic: The New Zealand Experience(Global Labor Organization, 2020-06-24) Rossouw, S; Greyling, T; Adhikari, TThis paper estimates Markov switching models with daily happiness (GNH) data from New Zealand for a period inclusive of the Covid-19 global health pandemic. This helps us understand the dynamics of happiness due to an external shock and provides valuable information about its future evolution. Furthermore, we determine the probabilities to transition between states of happiness and estimate the duration in these states. In addition, as maximising happiness is a policy priority, we determine the factors that increase happiness, especially during the pandemic to ensure rapid restoration of happiness levels post the Covid-19 shock. The results show New Zealand is currently in an unhappy state which is lasting longer than predicted. To increase the happiness levels to pre-pandemic levels, policymakers could allow free mobility, create economic stimuli, and allow international travel between New Zealand and low-risk Covid-19 countries.
- ItemSentiment, Emotions and Stock Market Predictability in Developed and Emerging Markets(Global Labor Organization, 2020-04-02) Rossouw, S; Greyling, T; Steyn, DThis paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements. The study contributes to the literature by analysing high-frequency data and comparing the results obtained from analysing emerging and developed markets, respectively. To this end, the study uses three different Machine Learning Classification Algorithms, the Naïve Bayes, K-Nearest Neighbours and the Support Vector Machine algorithm. Furthermore, we use several evaluation metrics such as the Precision, Recall, Specificity and the F-1 score to test and compare the performance of these algorithms. Lastly, we use the K-Fold Cross-Validation technique to validate the results of our machine learning models and the Variable Importance Analysis to show which variables play an important role in the prediction of our models. The predictability of the market movements is estimated by first including sentiment only and then sentiment with emotions. Our results indicate that investor sentiment and emotions derived from stock market-related Tweets are significant predictors of stock market movements, not only in developed markets but also in emerging markets.