This study analyzed the nonlinear impacts of education, particularly higher education, on economic growth in the ASEAN-5 countries (i.e., Thailand, Indonesia, Malaysia, Singapore, and the Philippines) over the period 2000–2018. The impacts of education on economic growth are assessed through various education indicators, consisting of public expenditure on tertiary education per student, enrolment rates of primary, secondary, and tertiary levels, educated workforce, and the novel indicator of unemployment rates with advanced education. This study establishes nonlinear regression models—the time-series kink regression and the panel kink regression—to investigate the kink effects of education on the individual country’s economic growth and the ASEAN-5 region, respectively. There are three main findings. Firstly, the nonlinear effects of the government expenditure per tertiary student on economic growth are confirmed for the ASEAN-5 region. However, the impacts do not follow the law of diminishing returns. Secondly, our findings reveal that an increase in unemployment of advanced educated workers can positively or negatively impact economic growth, which requires an appropriate policy to handle the negative impacts. Lastly, secondary and higher education enrollment rates can contribute to the ASEAN-5’s economic growth (both the individual and regional levels). However, the regional analysis reveals that higher education impacts become twice as strong when the enrollment rates are greater than a certain level (a kink point). Therefore, we may conclude that secondary enrollment rates positively affect economic growth; however, higher education is the key to future growth and sustainability.

This study aims to examine the relationship between economic development and environmental degradation based on the Environmental Kuznets Curve (EKC) hypothesis. The level of CO₂ emissions is used as the indicator of environmental damage to determine whether or not greater economic growth can lower environmental degradation under the EKC hypothesis. The investigation was performed on eight major international economic communities covering 44 countries across the world. The relationship between economic growth and environmental condition was estimated using the kink regression model, which identifies the turning point of the change in the relationship. The findings indicate that the EKC hypothesis is valid in only three out of the eight international economic communities, namely the European Union (EU), Organization for Economic Co-operation and Development (OECD), and Group of Seven (G7). In addition, interesting results were obtained from the inclusion of four other control variables into the estimation model for groups of countries to explain the impact on environmental quality. Financial development (FIN), the industrial sector (IND), and urbanization (URB) were found to lead to increasing CO₂ emissions, while renewable energies (RNE) appeared to reduce the environmental degradation. In addition, when we further investigated the existence of the EKC hypothesis in an individual country, the results showed that the EKC hypothesis is valid in only 9 out of the 44 individual countries.

The discussion on the use and misuse of p-values in 2016 by the American Statistician Association was a timely assertion that statistical concept should be properly used in science. Some researchers, especially the economists, who adopt significance testing and p-values to report their results, may felt confused by the statement, leading to misinterpretations of the statement. In this study, we aim to re-examine the accuracy of the p-value and introduce an alternative way for testing the hypothesis. We conduct a simulation study to investigate the reliability of the p-value. Apart from investigating the performance of p-value, we also introduce some existing approaches, Minimum Bayes Factors and Belief functions, for replacing p-value. Results from the simulation study confirm unreliable p-value in some cases and that our proposed approaches seem to be useful as the substituted tool in the statistical inference. Moreover, our results show that the plausibility approach is more accurate for making decisions about the null hypothesis than the traditionally used p-values when the null hypothesis is true. However, the MBFs of Edwards et al. [Bayesian statistical inference for psychological research. Psychol. Rev. 70(3) (1963), pp. 193–242]; Vovk [A logic of probability, with application to the foundations of statistics. J. Royal Statistical Soc. Series B (Methodological) 55 (1993), pp. 317–351] and Sellke et al. [Calibration of p values for testing precise null hypotheses. Am. Stat. 55(1) (2001), pp. 62–71] provide more reliable results compared to all other methods when the null hypothesis is false.

Lockdown policies have been implemented to reduce COVID-19 transmission worldwide. However, the shutdown of activities has resulted in large economic losses, and it has been widely reported that lockdown measures have resulted in improved air quality. Therefore, many previous studies have attempted to investigate the impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. Nevertheless, the heterogeneity among countries worldwide in the economic, environmental, and public health aspects and the spatial effects of decomposition have not been well investigated in the existing related literature. In this study, based on the cross-sectional data of 158 countries in 2020 and the proposed nonlinear simultaneous spatial econometric models, we investigate the nonlinear and spatial impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. The findings show that lockdowns have had statistically significant negative economic impacts and beneficial environmental consequences but no effect on COVID-19 spread. Noteworthily, this study also found the length of lockdown periods to affect the three domains of interest differently, with a piece of empirical evidence that the imposition of lockdowns for more than 31 days a year could result in economic impairments but contribute to environmental improvements. Lockdowns were shown to have substantially reduced PM2.5 not only in the countries that imposed the measures but also indirectly in the neighboring countries as a spatial spillover effect.

This study aims to investigate the causality and dependence structure of gold shocks and Asian emerging stock markets. The positive and negative shocks of gold prices are quantified, and Granger causality-based Vector autoregressive and Copula approaches are employed to measure the causality and contagion effect, respectively, between the positive and negative gold shocks and Asian emerging stock markets’ volatilities. In addition, the nonlinear link between gold and stock markets is of concern and this motivates us to propose a Smooth Transition Dynamic Copula that allows for the structural change in time-varying dependence between gold shocks and Asian stock markets’ volatilities. Several Copula families are also considered, and the best-fit Copula model is used to explain the correlation or contagion effects. The findings of the study show that there is some significant causality between gold shocks and Asian stock markets’ volatilities in some parts of the sample period. We also observe a stronger correlation during the global financial crisis when compared to the pre- and post-crisis periods. In addition, the tail dependence is found between Indian stock and negative gold shock and between Korean stock and negative gold shock, which indicated the existence of the risk contagion effects between gold and these two stock markets.