Uncertainty in Health Emergencies: Communicating Risks During COVID-19 Response

The world’s fragmented response to the COVID-19 pandemic created fertile ground for mixed messages and inconsistency. The authors analyzed Google-trending insights from five countries (Italy, Spain, the United States, the United Kingdom, and France) across three-week time (1–23 March 2020) to document trends in population health anxiety in response to the initial global spreading of the outbreak. The results are expressed in the form of Uncertainty Index (UI), which reflects the total number of Google searches/COVID-19 prevalence and standardized per million inhabitants. The United Kingdom experienced the highest level of health anxiety (UI = 11.5), followed by France (UI = 4.6) and Spain (UI = 3.2). The United States suffered the highest rate of uncertainty in the early stage of the pandemic; the Italian population experienced a balanced level of anxiety. Institutionalizing risk communication during COVID-19 should represent an integral part of the country emergency response.

The spillover effects of the COVID-19 pandemic: Which subsectors of tourism have been affected more?

This paper investigates the effects of COVID-19 pandemic-related uncertainty focusing on the US tourism subsectors, including airlines, hotels, restaurants, and travel companies. Using daily stock price data, we compute connectedness indices that quantify the financial distress in the tourism and hospitality industry and link these indices with a measure of COVID-19-induced uncertainty. Our empirical results show that some subsectors of tourism are affected more than others. The connectedness of tourism companies has severely increased after March 2020. Restaurants are the most heavily influenced subsectors of tourism, while airline companies come the next. Besides, our quantile regression suggests that higher quantile COVID-19 uncertainty index has more effect on the connectedness of tourism companies. Our results guide the policymakers and investors to detect the stress accumulated in each subsectors of tourism and to take more informed and timely decisions.

Policy Uncertainty, Economic Activity And Carbon Emissions: A Nonlinear Autoregressive Distributed Lag Approach

Over the last few years, economic uncertainty has become a global concern. Not only has its impact on economic activities, but there are pieces of evidence that show uncertainty can be the reason for CO2 emissions. It is also expected that the economic policy uncertainty may decrease or delay economic production, which may lead to a reduction in carbon emissions. Furthermore, uncertainty may decrease friendly environment policies and budgets, which cause increase in carbon emissions. Thus, there may be an asymmetric relationship between economic uncertainty and the amount of CO2 emissions. This study investigates the effects of economic policy uncertainty and economic activity on carbon emission applying a Nonlinear Autoregressive Distributive Lag (NARDL) cointegration approach in Iran between 1971 and 2018. Findings show that both policy uncertainty and economic growth contribute to CO2 emissions. The negative and positive shocks of GDP and uncertainty index on CO2 emissions in both the short-run and long-run are significant. It can be concluded that there is an asymmetric effect of economic production on CO2 emissions in Iran. The results of analyzing asymmetric effects of economic uncertainty show a symmetric relationship between uncertainty index and CO2 emissions. In a way that a shock in uncertainty index lowers carbon emission. To sum up, since uncertainty may affect the analysis of carbon emissions incorrectly, some environmental policies such as allocating a budget for R&D on clean energy, and environmental taxes must be implemented.

Dynamic frequency relationships between bitcoin, oil, gold and economic policy uncertainty index

Purpose This paper aims to examine the frequency of co-movements and asymmetric dependencies between bitcoin (BTC), gold, Brent crude oil and the US economic policy uncertainty (EPU) index. Design/methodology/approach The authors use a wavelet approach and a quantile-on-quantile regression (QQR) method. Findings The results show a positive interdependence between BTC and commodity price returns at both medium and low frequencies over the sample period. In contrast, the dependence is negative between BTC and EPU index at both medium and low frequencies. Furthermore, the co-movements between markets are more pronounced during crises. The results show that strategic commodities and EPU index have the ability to predict BTC price returns at both medium- and long-terms. The QQR method reveals that higher gold returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. Moreover, lower gold returns tend to predict lower (higher) BTC returns when the market is in a bearish (bullish) state (positive (negative) relationship). The lower Brent returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. High Brent quantiles tend to predict the lower BTC returns in its extremely bearish states. Finally, higher and lower EPU changes tend to predict lower and higher BTC returns when the market is in a bearish/bullish state (negative relationship). Originality/value There is generally a lack of understanding of the linkages between BTC, gold, oil and uncertainty index across multiple frequencies. This is, as far as the authors know, the first attempt to apply both the wavelet approach and a QQR method to examine the multiscale linkages among markets under study. The findings should encourage the relevant policymakers to consider these co-movements which vary over time and in duration when setting up regulations that deem to enhance the market efficiency.

Diffusion profile embedding as a basis for graph vertex similarity

We describe here a notion of diffusion similarity, a method for defining similarity between vertices in a given graph using the properties of random walks on the graph to model the relationships between vertices. Using the approach of graph vertex embedding, we characterize a vertex vi by considering two types of diffusion patterns: the ways in which random walks emanate from the vertex vi to the remaining graph and how they converge to the vertex vi from the graph. We define the similarity of two vertices vi and vj as the average of the cosine similarity of the vectors characterizing vi and vj. We obtain these vectors by modifying the solution to a differential equation describing a type of continuous time random walk.This method can be applied to any dataset that can be assigned a graph structure that is weighted or unweighted, directed or undirected. It can be used to represent similarity of vertices within community structures of a network while at the same time representing similarity of vertices within layered substructures (e.g., bipartite subgraphs) of the network. To validate the performance of our method, we apply it to synthetic data as well as the neural connectome of the C. elegans worm and a connectome of neurons in the mouse retina. A tool developed to characterize the accuracy of the similarity values in detecting community structures, the uncertainty index, is introduced in this paper as a measure of the quality of similarity methods.

PENGARUH UNCERTAINTY TERHADAP KEBIJAKAN TRADE CREDIT PERUSAHAAN INDONESIA

<p><em> </em><strong><em>ABSTRACT :</em></strong><strong><em> </em></strong><em>Uncertainty seems to be the root of prolonged recession period problem and it increases researcher concern regarding its effect to the economy. During high uncertainty period, information is not clear and affect firm’s decision maker regarding their investment, capital structure and also trade credit policy. In this paper, we aim to find the effect of uncertainty to firm’s trade credit policy. Uncertainty is argued to increase firm’s credit risk and thus, firm should adjust their trade credit policy to survive. Our sample includes non-financial listed firms in Indonesia Stock Exchange (IDX), with observation period from 2006 Q1 to 2019 Q4. The financial data used to construct the variables are obtained from the S&amp;P Capital IQ database. The methodology used is fixed effect panel data regression. This study utilized uncertainty measurement developed by </em>Ahir et al. (2018)<em>, that is the world uncertainty index (WUI). Using total of 12,773 firm-quarter observations, our result show that uncertainty indeed caused firms to adjust their trade credit policy. Uncertainty caused higher cost of capital, as consequences firms decide to tighten their credit policy to its customer and decrease their payables to the supplier as precautionary to avoid future financial distress. </em></p><p><strong><em>Keywords: </em></strong><em> </em><em>Uncertainty, trade credit policy, receivable days, payable days</em></p><p> </p><p><strong>ABSTRAK :</strong><strong> </strong>Ketidakpastian dianggap menjadi akar dari masalah periode resesi yang panjang dan hal ini meningkatkan kekhawatiran dari para peneliti mengenai efeknya terhadap ekonomi. Pada keadaan ketidakpastian yang tinggi, informasi menjadi tidak jelas dan mempengaruhi pengambil keputusan didalam perusahaan mengenai kebijakan investasi, struktur modal dan juga kredit dagang. Pada penelitian ini, kami bertujuan untuk menemukan efek dari ketidakpastian pada kebijakan kredit dagang perusahaan. Ketidakpastian dikatakan dapat meningkatkan resiko kredit perusahaan dan sehingga, perusahaan perlu menyesuaikan kebijakan kredit dagang mereka untuk dapat bertahan. Sampel penelitian ini termasuk perusahaan non keuangan yang terdaftar pada bursa efek Indonesia (BEI), dengan periode observasi dari 2006 kuartal 1 hingga 2019 kuartal 4. Data keuangan yang digunakan untuk membentuk variabel pebelitian diambil dari <em>the S&amp;P Capital IQ database. </em>Metodologi yang digunakan adalah regresi data panel <em>fixed effect</em>. Penelitian ini menggunakan ukuran ketidakpastian yang dikembangkan oleh Ahir et al.(2018), yaitu <em>the world uncertainty index (WUI)</em>. Dengan menggunakan total observasi sebanyak 12.773 perusahaan-kuartal, hasil yang didapatkan menunjukkan bahwa ketidakpastian mennyebabkan perusahaan untuk melakukan penyesuaian pada kebijakan kredit dagangnya. Ketidakpastian meningkatkan biaya modal dan sebagai akibatnya perusahaan memutuskan untuk memperketat kebijkan kredit kepada pembeli dan menurunkan hutang dagangnya pada pemasok sebagai tindakan berhati-hati untuk menghindari kesulitan keuangan dimasa depan.</p><p><strong>Kata Kunci:</strong> Ketidakpastian, kebijakan kredit dagang, <em>receivable days, payable days</em></p><strong></strong>

The Uncertainty Index and Foreign Direct Real Estate Investments in Developing Economies

Attainment of standards in a country’s real estate market to meet international investors’ expectations contributes significantly to the real estate sector. However, in developing economies characterized by an environment of uncertainty where stability cannot be achieved, direct investments in real estate can bring returns to foreign investors. This is because economic uncertainty in developing countries raises the exchange rate. An increase in the exchange rate keeps real estate prices in developing countries relatively low. Foreign investors then take advantage of the low prices to invest in real estate in that country. The study aims to research whether the uncertainty in developing countries increases the foreign direct real estate investments. The study examines the relationship between the uncertainties in selected developing economies in Europe and the real estate investments by foreigners in the period 2008–2018. Gengenbach, Urbain, and Westerlund Panel Cointegration test and PDOLS coefficient estimation methods were used in the study. According to the analysis results, a 1% increase in the uncertainty index in the economies examined increases foreign direct investments by 5.731%. Since this study is one of the most detailed studies measuring foreign direct real estate investments under uncertainty conditions in the economy, it contributes to the literature. To sustainably increase foreigners’ direct real estate investments in developing countries, economic and political stability should be prioritized. Facilitating the bureaucratic process, providing tax reductions, making real estate suitable for demand, following the appropriate price policy, and making various environmental regulations will also increase foreigners’ direct real estate investments. Doi: 10.28991/esj-2021-01293 Full Text: PDF