Scientific Annals of Economics and Business
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Globalization and Per Capita Income Growth in Emerging Economies
Get PDFIn this study, the efficacy of globalization in influencing income growth within the Sub-Saharan Africa (SSA) from 1982 to 2020 is being examined. The “Konjunkturforschungsstelle Globalization Index” (KOFGI) was used to measure globalization at the overall, economic, social, and political level, while income growth was captured using the growth rate of gross national income per capita. The data employed in the analysis were gotten from World Bank and KOFGI database. The analysis follows a sequential order of unit root test based on the augmented Dickey-Fuller, autoregressive distributed lag (ARDL) bounds test for cointegration, and error correction model. The unit root test revealed that the order of integration of the variables were mixed at levels and first difference. The bounds test showcased that all the dimensions of globalization exhibited long-run association with income growth. The short-run result indicated that globalization wielded a negative and significant effect on income growth. A unit percent increase in globalization put forth a 1.3818% decrease in income growth. In the long-run, globalization however exerted a positive but insignificant sway on income growth in the SSA. The implication of this is that though globalization poses a short-run negative impact on income growth, the SSA can move along the learning curve to derive some long-term benefits that emanate from global interactions. It becomes pertinent for the SSA to see globalization as a long-term avenue for propelling income growth, bearing in mind that the short-run negative effect can be corrected periodically as the economy moves along the learning curve of globalization
Time-Series Momentum in a Small European Stock Market: Evidence from a New Historical Financial Dataset
Get PDFIn this paper, we examine the Portuguese stock market for indication of time-series momentum effects using a new historical financial dataset that covers about 120 years of data. We find strong time-series momentum effects that cannot be explained by conventional risk factors. The positive return continuation seems to last for a period of 12 months, being heavily concentrated at the first month. At longer investment horizons, returns tend to mean-revert. The market exhibited significant time-series momentum for all look-back and holding periods of 12 months or less. A strategy with a 1-month look-back period and a 12-month holding period is shown to be the most profitable yielding a Sharpe ratio roughly 5.4 times that generated by a passive strategy. Time-series momentum strategies tend to perform best during extreme up-market periods and deliver the worst returns during down markets. This suggests that the strategy may not offer significant diversification benefits. Our findings add to the evidence that time-series momentum effects are not a product of data mining and are difficult to reconcile with the assertion that stock markets follow a random walk
Flip the Coin: Heads, Tails or Cryptocurrencies?
Get PDFThis paper analysis and compares the volatility of seven cryptocurrencies – Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, Stellar and Litecoin – to the volatility of seven centralized currencies – Yuan, Yen, Canadian Dollar, Brazilian Real, Swiss Franc, Euro and British Pound. We estimate GARCH models to analyze their volatility. The results point to a considerably high volatility of cryptocurrencies when compared to that of centralized currencies. Therefore, we conclude that cryptocurrencies still fall far short of fulfilling all the requirements to be considered as a currency, specifically regarding the functions of store of value and unit of account
The Empirical Study of the Impact of Firm- and Country-level Factors on Debt Financing Decisions of ICT Firms
Get PDFThe capital structure has been extensively analysed in the empirical literature. Despite of the great contribution of the technological industry to the global economy, little research has been conducted regarding corporate finance of ICT firms. Moreover, the previous literature barely considers the effect of macroeconomic variables on financial decisions, focusing much more on internal determinants, such as cash flow, firm’s size or growth opportunities. The objective of this work is to reduce this gap by disentangling the reasons behind the financial decisions of technological firms. The sample included 1,510 public ICT firms from 23 countries over the period 2004 – 2019 (17,342 observations). The variables used in this study are obtained from S&P Capital IQ, World Development Indicators, Main Science and Technology Indicators from OECD, and FMI dataset. The two-step system generalized method of moments (GMM) was used as methodology. Consistent with the extant literature, more profitable and liquid ICT firms and those with an increased non-debt tax shields are less leveraged. However, the companies which present higher risk, measured as volatility of EBIT, increase their use of debt financing. Contrary to the findings of many other studies, the analysis of a firm’s size and tangible assets shows non-conclusive results. Regarding macroeconomic determinants, only economic growth and foreign direct investment inflows were found to generate a positive effect on financial decisions of ICT firms. The findings of this work can be used to design and develop policies, measures, and facilitate mechanisms for optimal management of the financing decisions of ICT firms
Resilience to Online Privacy Violation: Developing a Typology of Consumers
Get PDFThis study examines which segments of population with similar resilience to online privacy violation, severity of online privacy violation, and attitudes towards online privacy concern exist in Croatia, and whether they can be differentiated by demographic characteristics and attitudes towards other online constructs. Research is performed on a representative sample of Croatian Internet users who experienced online privacy violation. The survey data were analyzed using factor analysis, k-means cluster analysis, chi-square test and ANOVA. The findings indicate three groups of consumers with: (1) low-resilience, (2) moderate-resilience, and (3) high-resilience; who differ in age, income, and online buying habits
Worldwide Fiscal Progressivity: What can we Learn from Subjective Wellbeing Economics?
Get PDFThe link between fiscal progressivity and subjective well-being at global level is an issue that has hardly been considered in the literature on the Economics of Happiness. Oishi et al. (2012) is almost the only work in this field, and they concluded that those countries which had more progressive income tax systems were also happier. Our work use their definition of progressivity as the difference between the upper and lower marginal rate on income, in order to prove its relationship with subjective well-being (SWB), but we have observed that such indicator is not very significant for a sample of 111 countries. Besides, we conclude that the fact that a country's maximum income tax rate is high turns out to have a strong influence on the declared subjective well-being of its citizens. One possible explanation for it could be that they are countries with a high GDP per capita in which disposable income after taxes remains high. However, it must be taken into account that in our work we have managed to isolate the influences that the GDP per capita variable could have using the principal component analysis method
The 'Bad Behavior Index': A Composite Measure of the Development Hindering Behavior of Individuals and Institutions
Get PDFComposite indices have become a popular tool for providing a quantitative, simplified, and visualized representation of complex phenomena. An example of such is the Human Development Index (HDI) which ranks countries by their level of development. The primary limitation of the HDI is its narrow scope, which hinders its effectiveness at explaining why some nations are more developed than others. The discussion as to why some nations are more developed than others goes back as far as the 14th century, where Ibn Khaldun developed a theory which aims to explain why civilizations rise and fall. Some of the hypotheses which seek to answer this question point to the importance of economic freedoms, absence of corruption, high investment in human capital, and the importance of institutions etc. to development. One hypothesis which has not been properly studied regards the culpability of individual and institutional behavior. The purpose of this study is to introduce a composite measure of the development hindering behavior of individuals and institutions, i.e., the Bad Behavior Index (BBI). The methodology of this study is influenced by the Mazziotta & Pareto framework for composite indices. The index weights have been computed by integrating expert opinion with the Fuzzy Analytic Hierarchy Process (FAHP). The findings of this study suggest that African countries engage in the highest level of bad behavior, which subsequently leads to their poor socio-economic development, whereas Northern countries engage in the least level of bad behavior. The study also finds that the most important drivers for socio-economic development are low levels of corruption, high levels of knowledge creation, strict application of the rule of law, high levels of social cohesion, and high levels of political stability
Does Foreign Direct Investment and Trade Openness Support Economic Development? Evidence from Four European Countries
Get PDFThe European Union (EU) as a political and economic union has provided many benefits to its member states through the single market and common tariffs that serves as a platform for internal trade and international trade with third-world countries. The study aimed to investigate the effect of foreign direct investment (FDI) and trade openness on economic development in four selected countries including the Czech Republic, Estonia, Lithuania, and Slovakia using panel data from 1995 to 2021. The data was obtained from the World Bank and analyzed through econometric methods such as pooled model, fixed effect model, random effect model, and the dynamic panel model. The between transformation results using the pooled ordinary least squares indicated that the Czech Republic had the highest intercept coefficient, followed by Slovakia, Lithuania, and Estonia, respectively. The panel specification test discovered that the pooled model was inadequate, and the random effect model is the most appropriate to be used. The results from the random and fixed effects models displayed that FDI and trade openness have a positive impact on economic growth in these countries. Additionally, the dynamic panel outcome proved a positive effect of FDI and trade openness. The study recommends that governments in these countries improve their business environment to attract more FDI and trade relations with other countries
The Infancy of the Esports Industry as a Risk to its Sponsors
Get PDFIn less than 10 years, esports have turned into a global phenomenon with a large following that rivals the audience size of popular established sports. This has resulted in a massive influx of esports sponsors. However, because it appeared and evolved so rapidly, sponsors have no idea of what esports really are nor of what risks they may face. Ergo, this research aimed to determine what issues are being caused by the infancy of the esports industry that is threatening sponsors. Hence, this exploratory research used a convergent-parallel mixed method with equal status. Empirical data was obtained through interviews with 22 experts in esports sponsoring and the application of a survey to 5,638 esports fans. Quantitative data was analysed with SPSS 25 and qualitative data with NVIVO 10. The results showed that the majority of experts considered that the problems associated with the infancy of the esports industry are a risk to esports sponsors and almost all esports fans reckon that the competitive gaming market has infancy-related issues to solve. Esports are not like general sports, so sponsors must holistically study this industry to mitigate the dangers of suffering from the problems of this new and unknown market
Modelling the Non-Linear Dependencies between Government Expenditures and Shadow Economy Using Data-Driven Approaches
Get PDFThis article aims to model the relationship between the size of the shadow economy and the most important government expenditures respectively social protection, health, and education, using nonlinear approaches. We applied four different Machine Learning models, namely Support Vector Regression, Neural Networks, Random Forest, and XGBoost on a cross-sectional dataset of 28 EU states between 1995 and 2020. Our goal is to calibrate an algorithm that can explain the variance of shadow economy size better than a linear model. Moreover, the most performant model has been used to predict the shadow economy size for over 30,000 simulated combinations of expenses in order to outline some possible inflection points after which government expenditures become counterproductive. Our findings suggest that ML algorithms outperform linear regression in terms of R-squared and root mean squared error and that social protection spending is the most important determinant of shadow economy size. Further to our analysis for the 28 EU states, between 1995 and 2020, the results suggest that the lowest size of shadow economy occurs when social protection expenses are greater than 20% of GDP, health expenses are greater than 6% of GDP, and education expenses range between 6% and 8% of GDP. To the best of the authors' knowledge, this is the first paper that used ML to model shadow economy and its determinants (i.e., government expenditures). We propose an easy-to-replicate methodology that can be developed in future research