Environmental consequences of population, affluence and technological progress for European countries : A Malthusian view

This paper examines the short-run and long-run effects of economic, sociological and energy factors on environmental degradation in 28 European countries. In so doing, we employ Panel Vector Autoregressive (PVAR) and Fully Modified OLS (FMOLS) approaches on data from 1990 to 2014 in a STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework. Key empirical results indicate that these factors may contribute to environmental improvement in the short run; however, there are adverse implications in the long-run. Specifically, economic factors including economic growth, trade openness and foreign direct investment cause environmental degradation in the under-analysis economies. The sociological factors as measured by the population growth and the level of urbanization also show a negative impact on the environmental degradation in the short-run but in the long run, both population size and urbanization increase environmental degradation. These findings are in line with the concerns raised by Thomas Robert Malthus in his Essay on the Principle of Population. With regards to the energy factors, it indicates that the renewable energies help the European environment by reducing the level of carbon dioxide emissions whereas the higher energy intensity is an ecological threat. Our results remain robust in the EKC framework

The nexus between black and digital gold: evidence from US markets

In the context of the debate on cryptocurrencies as the ‘digital gold’, this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments

Financialisation of Natural Resources & Instability Caused by Risk Transfer in Commodity Markets

Understanding the connectedness of financial markets and hence possible sources of systematic risk is central to the debate on the process of financialisation and its consequences for financial stability. In this study, we examine the connectedness between commodity spot and futures prices by applying a novel frequency connectedness framework on data from January 1979 to December 2019 to measure the connectedness among financial variables. Focusing on the seven most widely traded commodities, including gold, silver, crude oil (WTI and BRENT), corn, soya and iron, we find that (i) volatility of the commodity derivatives (futures) contribute to the spot volatility and hence influence spot prices of the underlying commodities in international markets (ii) volatility spillover effects are stronger in the first four days of the shock, suggesting that shocks to the underlying asset volatility caused by its own fundamental are more prevalent and persistent in the long-term (iii) commodities futures volatility transmission is higher than spot price volatility transmission to the futures prices. Our findings shed new light on the relationship between the actual spot price of commodities and their derivatives and have crucial socio-economic implications in terms of financialisation of important commodities

Anchoring Inflation Expectations in the Face of Oil Shocks & in the Proximity of ZLB : A Tale of Two Targeters

This paper applies a N-ARDL framework to two longstanding inflation targeting policy regimes in order to assess the relation between oil prices dynamics and inflation expectations and the further consequences created by a proximal ZLB situation. The application is based on data from January 1994 to June 2018 for New Zealand and the UK. We focus on oil price shocks as a variable of interest and this was found to have an asymmetric effect on inflation expectations. One further key finding is that the real effective exchange rate has significant impacts on inflation expectations and this is indicative of an exchange rate pass-through to inflation via an inflation expectations channel. In general, we find that inflation, exchange rate, money supply, output growth, unemployment and fiscal deficit/surplus have significant implications for inflation expectations. Inflation expectations are also influenced by their past behaviour indicating adaptive inflation expectations. This study contributes to the debate on the inflation targeting at ZLB

Information Asymmetry and firm value : Is Vietnam different?

© 2019 Elsevier B.V. Using firm-level data from 250 non-financial companies with 2,500 firm-year observations collecting from two stock exchange markets in Vietnam covering a 10-year period from 2008 to 2017, this paper examines the relationship between information asymmetry and firm value in Vietnamese firms. The findings reveal that fundamentally, information asymmetry in Vietnamese firms has a negative impact on firm value. Although this conclusion is consistent with that in the literature underlying by pecking order and agency cost theories, the value of information asymmetry related variables is higher than that in similar studies conducted in other developed countries. The results also find that the financial leverage in Vietnamese firms is higher than in other developed countries but can only play a limited role in mitigating the negative impact of information asymmetry on firm value. All in all, the findings relating to all variables used in the study highlight that Vietnam is a typical emerging country because there is a precise distance from other developed countries

Forecasting cryptocurrency returns and volume using search engines

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers

Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes

Background: Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of severe/moderate dengue (S/MD). Methods: We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included. Results: On days 1–3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults. Conclusions: Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients. Funding: This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Published versio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

National identity predicts public health support during a global pandemic

Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics