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Governments around the globe have taken different measures to tackle the COVID-19 pandemic. The city of Santiago, Chile, followed a dynamic lockdown strategy in which its municipalities were temporarily restricted depending on the degree of advance of the pandemic. We use this variation over time to study the effect of lockdowns on public transport demand, by analysing a 2019-2020 database of smartcard data of trips. These data allow us to identify public transport trips at a bus stop level,accounting for the variation of municipalities that were under lockdown in a given day. Metro and buses are analysed as individual modes and combined, allowing to estimate mode-specific factors. Using an empirical strategy that controls for timely variations, we find that dynamic lockdowns reduced public transport demand for all the public transport modes, but the average effect was larger for buses, which may be explained in part by fare evasion for this mode of transport and/or socioeconomic differences on mode usage. We also find that this effect varies over time. After the fifth week of the application of the lockdowns, its effect starts slowly vanishing, which suggests short-term effectiveness of such policy to reduce transport mobility in this case. Regarding sociodemographic effects, we find that lockdowns have a stronger impact in reducing public transport demand in municipalities with a larger proportion of elderly populationand high-income households.

Global problems require global scientific solutions, but the dominance of the English language creates a large barrier for many non-English-proficient researchers to make their findings and knowledge accessible globally. Here, we propose integrating peer language proofing and translation systems in preprint platforms as a solution for promoting equity, diversity, and inclusion in science.

In this study we report on our experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. We made use of GENSI, a recently developed tool for network data collection using interactive visual elements that has been shown to reduce respondent burden. A sample of 758 women between the ages of 18 and 40 were recruited through the LISS-panel; a longitudinal online survey of Dutch men and women. Respondents were asked to name exactly 25 alters, answer sixteen questions about these alters (name interpreter questions), and assess all 300 alter-alter relations. Nearly all (97%) respondents reported on 25 alters. Non-response was minimal: 92% of respondents had no missing values, and an additional 5% had fewer then 10% missing values. Listing 25 alters took 3.5±2.2 (mean±SD) minutes, and reporting on the ties between these alters took 3.6±1.3 minutes. Answering all alter question took longest with a time of 15.2±5.3 minutes. The majority of respondents thought the questions were clear and easy to answer, and most enjoyed filling in the survey. Collecting large personal networks can mean a significant burden to respondents, but through the use of visual elements in the survey, it is clear that it can be done within reasonable time and enjoyment and without much non-response.

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you.Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

The objective of this study was to assess how 14 large and established scientific publishers have adopted the use of preprints and how their policies changed in a one-year period between June 2017 and June 2018, if any. The core search was performed using the Sherpa/RoMEO database. Of all publishers (2516, now 2553) listed in the RoMEO database, 80.3% of the publishers examined allow self-archiving, but only half of the publishers (47.3%) allow the archiving of preprints in February of 2018, while this percentage increased to 48% in June 2018. These data were practically constant over a one-year period even as the number of preprint servers has increased. Several exceptions exist among journals within each of the tested Sherpa/RoMEO-indexed publishers, and in some cases, a reversal in policy was observed, i.e., from allowing to no longer allowing the archival of preprints.

Copyright: 2018 Elsevier. Due to copyright restrictions, the attached PDF file contains the preprint version of the published item. For access to the published article, please consult the publisher's website: https://www.sciencedirect.com/science/article/pii/S2352710218309033

This is a short review and commentary on a paper that was recently published, regarding the employment of an Artificial Intelligence system for targeted testing of travellers for SARS-CoV-2 at the border checks during the summer of 2020 in Greece. The core thesis of the paper is that the system offered significant accuracy and risk mitigation mechanisms for this task. However, the methodology and the supportive material presented therein shows several deficiencies and biases that do not properly justify such claim. The main problems with the paper are presented here, with short discussion about how they should be addressed by the authors.

The 2020 COVID-19 driven recession saw a sharp drop in carbon dioxide emissions as transportation and some other energy uses were curtailed. This was an unusual recession as it was driven by a pandemic. Previous research shows that when GDP declines carbon emissions fall faster relative to GDP than they rise in economic booms. Using monthly US data, we examine each individual recession in the US since 1973 finding that there is an asymmetric response in the 1973-5, 1980, 1990, and 2020 recessions but not in the 1981-2, 2001, or 2008-9 recessions. The former four recessions are associated with negative oil market shocks. In the first three there was a supply shock and in 2020 a demand shock. Changes in oil consumption that are not explained by changes in GDP explain these asymmetries. Furthermore, the asymmetries are due to emissions in the transport and industrial sectors, which are the main consumers of oil.

This policy paper assesses the financial and operational effectiveness of the government's efforts to reduce the negative impact of the COVID-19 pandemic on business and strengthen the social protection of the poor during this period, and puts forth proposals for relevant government agencies. The research for the preparation of this document studied the response to the COVID-19 pandemic in foreign countries, especially those rich in natural resources, and the best practices of developed countries. The paper ends with a section on conclusions and proposals.

On 5 October 2020, during the COVID-19 pandemic, the Indonesian National Legislature passed a very controversial ‘Omnibus Law’ on Job Creation. This legislation, with a massive 1187 pages, was then signed by President Joko Widodo and came into effect on 2 November 2020 and is now officially known as Law no. 11/2020 on Job Creation (Undang-Undang no. 11/2020 tentang Cipta Kerja).This Paper undertakes the task of piecing together the labour cluster of amendments in the Omnibus Law on Job Creation and aims to explain and analyse these changes in the context of the previous law.

The thesis of this paper is that the initial response to COVID-19 in the U.S. was hampered by structural weaknesses in the U.S. political and healthcare systems that caused them to be ill-adapted to deal decisively with a pandemic on the scale of COVID-19 but presumably have numerous advantages and offsetting benefits in other areas. These structural features did not inevitably doom the U.S. to respond as it did; individual actors in politics, the administrative state, and the media all could have behaved differently. Rather, those features constituted weaknesses that created incentives for the counter-productive but predictable behaviors that we describe. In the long run, however, the U.S.’s technological and administrative ability to develop and deploy an effective vaccine may eventually overcome some of the U.S.’s initial administrative problems and mis-steps, but not until after we suffered a large number of deaths and illnesses, some of which might have been avoided by better planning and preparation. This pattern is not atypical of other responses by the U.S. to crises, which has been compared to that of a “sleeping giant” that is slow to awaken but can deploy strong measures once it does. One over-riding lesson to be learned from the U.S. response to the COVID-19 pandemic is “Don’t try to hold a U.S. presidential election while trying to contain a pandemic.” The on-going presidential election made it very difficult to coordinate a unified national response.

The digital world is one of the fastest developing sections of the technological based being observed with these ever developing sectors mainly focusing on the periods of the COVID-19 pandemic and the 2021 General Elections in Uganda. In this era of fast assess whether digital rights and freedoms are being respected in areas such as these, mainly focusing on the COVID-19 pandemic and the 2021 General Elections. compositions world-wide, with a recent breakthrough in the quantum computing world the digital space is still on arise thus a need to assess whether digital rights are Internet of Things, so with all this at the back of the mind there is great need to internet speeds sponsored by 5G connectivity, quantum technology, Big Data and the more so in a country like Uganda where such technology is still or yet to develop.

In this paper, we quantify the impact of COVID-19 on customer purchase behaviors -- customer acquisition, retention, ordering, and spending -- within the restaurant food delivery category in the United States and assess the mechanisms through which these effects have arisen using a unique collection of data sources. Our results suggest that pre-pandemic customer purchase trends were unfavorable, with falling acquisitions and weakening cross-cohort repeat purchase dynamics. COVID-19's impact has been significant, creating $19.3 billion in incremental sales for the category in 2020, or 69% of the overall year-on-year increase in sales. This increase was primarily due to higher purchase frequency from already-active pre-COVID customers and an increase in average order size, not due to changes in customer acquisition and retention. Turning to mechanisms, we find that this growth is primarily attributable to substitution away from restaurant dine-in; while increased stay-at-home behavior has increased customer adoption and order size, it has actually dampened overall sales growth. These results call into question the long-run sustainability of the pandemic-fueled growth in delivery sales, should on-premise dining meaningfully recover after it returns to being a safe activity.

The study focuses on examining the impact of the COVID-19 supply shock on the Indian macroeconomic variables. Time Varying Factor Augmented Vector Auto Regressive Model (TVP-FAVAR) has been employed for the econometric analysis. We found that with supply shock, retail food inflation outpaced in COVID times. Production levels reported by IIP fell to abysmally low levels in the post-COVID times, when the economy stalled. The liquidity stimulus provided by the central bank led to the negative response of policy rates to the supply shocks during the COVID times. The paper suggests the central bank to focus on sector-specific liquidity injections and take appropriate steps to evacuate fluctuating generic liquidity in the Indian economic system. This will reduce food inflation and help small non-food related industries to bounce back.

We present evidence that multi-market banks transmitted economic shocks relating to the COVID-19 crisis among the geographic areas where the banks operate. We find that banks transmission effects were about half as economically significant as the direct effect of the shocks. We also find that the presence of community banks in a geographic area mitigates multi-market bank transmission effects. Our results illustrate the importance of banks for local economic outcomes, and the role of community banks in mitigating the transmission of economic shocks. Our results also suggest that policy responses to economic shocks should be informed by the structure of the geographic network of the local banks.

This paper aims to analyze the gender behavior in the Brazilian labor market due to the economic effects of the COVID-19 pandemic. The focus is on the jobs destruction or creation during the lockdown and social distancing implementations, covering the behavior all months of 2020. For this, we use the New General Register of Employed and Unemployed (NCAGED) and apply the Oaxaca-Blinder decomposition between the female and male net destruction jobs at municipal levels, considering individuals with 18 to 40 years. Furthermore, we verify the inequality for every month of 2020, pre-and post-implemented in pandemic context. We found evidence that the initial months were harmful to all formal workers but had even greater weight on women. Thus, another relevant contribution is in the inequality decomposition, where the findings show that it is largely due to structural effects.

The understanding of teaching and learning, jointly with training along with decision methodologies, was the primary to be alive valuable through these conclusions. Merely a scattering of classified schools possibly will take up the online training process. Their less-income of private plus government school complements, therefore, on the other hand, include a total blackout meant for not having the right of entry in the direction of e-learning way-outs. The learners, inside adding together on the way to the let passed chances for knowledge, no more longer have entree to in tasty physical shape meals for the duration of this time and are area under discussion matter to monetary and communal strain. Objective- To discuss the current impact of COVID-19 on education and to explore the difficulties in network connectivity. Method-The method was used for this study is the online survey by the investigator. Result-The researcher received the suggestions for online teaching after the lockdown;only 10% of respondents were in the favour of continuing online teaching.

We evaluate sovereign default risk in G7+5 countries under the novel coronavirus disease (COVID-19) crisis in 2020. A second COVID-19 wave has been spreading worldwide since the end of 2020. We first derive default risk parameters using sovereign credit default swap spreads and constant maturity treasury yields. In all countries except Canada, China, and South Africa, default probability and hazard rate curves peaked early in the first wave. Many COVID-19-related fiscal policies heighten sovereign default risk. Second, we explore two correlation-based networks associated with a metric distance. This study extends financial research on sovereign default risk management under a pandemic.

This paper proposes a cloud manufacturing model as a supply chain approach for the post-COVID-19-economy. A fuzzy inference system is integrated into the model to optimally manage uncertainties related to Time, Quality, Cost, Reliability, and Availability caused by disruptions in the supply chain. The paper illustrates the implication of the model using a simulation in the context of a manufacturing process. The proposed model seeks to satisfy customers’ demands for high-quality products and services, in a timely manner, at the lowest cost possible. These properties are necessary in times of disruptions such as the COVID-19 pandemic and the like.

We study how investor sentiment responds to the prevalence of COVID-19 induced equity market volatility. Using the quantile-on-quantile approach, we report a strong relationship between sentiment and volatility. We note that low to medium volatility yield minimum fear, with high volatility triggering extreme fear in the crypto market.

This study uses the technique of inoperability input-output analysis to estimate the impact of COVID-19 pandemic on India. The study estimates that the complete lockdown of transport and hotel sectors to prevent the spread of coronavirus resulted in a loss of 4.2% of the output at basic prices. Sector by sector estimates of total loss, loss to labour and capital income and dispersion of loss between rural and urban areas is also estimated.

This study aims to contribute to the critical discourse on project management processes within the vein of institutional theory. Specifically, the study will focus on the dynamics between disruptive elements and agility which lead to process and organisational change. This empirical research will focus on how project professionals have had to adapt and adopt new organisational routines, particularly following COVID-19. The global coronavirus pandemic has had a profound and dramatic impact on working practices, especially in professional occupations who rapidly shifted to home-based working. Individuals, industries, and organisations of all sizes have had to re-evaluate their management processes and project priorities as a result of the disruptive impact of the pandemic crisis.

COVID-19 has launched a flurry of contractual questions. Commercial leases during the pandemic present a unique challenge to the existing Canadian common law, renewing the doctrinal debate on the differences between hardship and frustration. This paper will argue that the doctrine of frustration, in its current form, might be an inadequate legal tool to deal with COVID-19 contractual challenges in Canada. I will begin by defining key terms. Next, I consider the law of frustration and its limited but powerful remedial effect to discharge a contract. I then survey complementary changed circumstance regimes in the United States, Germany, the United Nations Convention on Contracts for the International Sale of Goods (CISG) and the UNIDROIT Principles of International Commercial Contracts (UPICC). In the last section, we will see that UPICC has influenced Quebec civil law’s approach to changed circumstances by incorporating the principle of good faith. Although not authoritative in Canadian common law, the final section will show that these alternative regimes may prove persuasive in expanding the doctrine of frustration's remedial effects to include renegotiation. This may enable Canadian contract law to better respond to issues emerging from the current global pandemic.

COVID-19 has accelerated in transition to a more fragmented world order in which the future organizing principles of the international system are unclear. Analysis of the Post-COVID world tends to focus on one of two poles. For some, the pandemic is a crisis that will reorganize the decks and fundamentally reorganize world politics. For others, the basic principles of the international order are likely to remain largely the same, driven primarily by the emerging bipolar system between the United States and China. This paper argues that the pandemic exposes underlying trends to global changes in the Global power pole, forcing a new path toward Globalization.

It is said that a strategy to contain the COVID-19 pandemic is to “follow the Science”. This paper argues that following the Science, although very important, is secondary to following the probability of infection or death, however small that probability might appear to be at a given time. Following the Science is difficult, because it takes time to understand phenomena; and there are many hurdles to overcome in order to effectively disseminate it to the people. It is far easier for a Leader to invoke the St. Petersburg Paradox (SPP) and, by being a Teacher to the people, disseminate the dangers of staking too much by ignoring small probabilities. The SPP, which has remained, for more than 300 years now, an important and unsolved problem in Economics and Philosophy, vividly, and simply, teaches us to not assume that we will emerge victorious when facing small probabilities. It is argued that a Leader facing small probabilities, generated by this most formidable pathogen, can mitigate a very large loss by urging them to pay the relatively small cost of taking preventative measures to combat the pathogen. In the presence of such small probabilities, Science can never reach the heart of a nation as effectively, and speedily, as can a Leader invoking the SPP. Although it is human to be impatient, a small probability can grow explosively in a short time, leaving unimaginable losses in its wake, if there is no Teacher to alert us to that fact.

Presently, the global focus is on the impact of the COVID-19 pandemic to all aspects of human life, which in this study, on educational perspectives. This study aimed to assess the initial implementation of the Flexible Learning Modality of Pangasinan State University Lingayen Campus during the School Year 2020-2021. It explored on the issues and challenges faced by both teachers and students during flexible instruction. Using extracts from the responses of teachers (n=123) and students (6370) in the survey questionnaire, the findings showed that an unstable, weak and poor internet connection and no comfortable teaching and learning spaces at home appear to pose as greatest barriers for online learning to both the faculty and students. Further, the study noted that PSU was quick to implement an online learning platform but found that the gadgets available for use by a number of the respondents could not run the app or at least run its full features, and which led to another challenge found on the difficulty to download teaching and learning resources using the software. The researchers concluded that the PSU Lingayen Campus was prepared in aspects under its control including provision of and orientation to online platforms, and plans to provide printed guides, but have an understandable lacking on challenges met including teaching and learning spaces, poor internet connection, and the capacity to purchase mobile load as means of students to attend classes online. The researchers recommend periodic assessment of the PSU’s policies for Flexible Learning to provide continued and improved service delivery for both the faculty and students.

Any form of flexible learning necessitates an assessment of technological preparedness of the stakeholders. This study aimed to assess the technological preparedness of Pangasinan State University (PSU) as it implements flexible learning modality (FLM). Participated in by nearly 20,000 students through a survey conducted during the School Year 2020-2021, findings showed a general technological preparedness of the students for online learning. Main challenges found included intermittent power interruptions in various municipalities in Pangasinan, Philippines during the early phase of FLM, as well as poor internet connection. Issues found included costly purchase of mobile data, stress on multiple online submissions of requirements, preference of printed study guides, no learning spaces at home, and technical difficulties on the use of the online learning platform used by PSU. Recommendations include re-alignment of unused funds on printing services for the purchase of mobile data for qualified students, synchronous submissions of requirements online per college, and task-based orientation on the use of the platform.

Since April 2020, the U.S. Census Bureau has run the Small Business Pulse Survey to measure COVID-19 impact on small employer businesses: firms with at least 1 but no more than 500 paid employees with receipts above $1,000. The most recent public data were collected in October 2021 and unfortunately do not contain information on the owners’ race and ethnicity. However, Census is adding owners’ demographic information and has so far published data up to early January 2021. This paper introduces a simple methodology to overcome the lack of public data broken down by race and ethnicity and timely assess whether COVID-19 has affected small minority-owned employer businesses more or less than their non-minority counterpart by leveraging non-demographic SBPS data as soon as they are published. The results show that minority-owned businesses did indeed suffer more from COVID-19. It is possible — but not certain — that these results underestimate the true effect of COVID-19 on minority businesses as compared to non-minority businesses

Federal law allows certain state and local government employees to be excluded from Social Security if they are covered by an employer pension of sufficient generosity. As a result, approximately one-quarter of state and local workers are not covered by Social Security on their current job. Before COVID-19, these “FICA replacement plans” all satisfied the letter of the law in terms of providing benefits of sufficient generosity. This study has three aims. The first is to document the immediate impact of COVID-19 on the financial status of FICA replacement plans. The second is to investigate whether COVID-19 has led to cuts in benefit promises among plans, as well as the likelihood of future cuts. The third is to investigate the likelihood that FICA replacement plans will exhaust their trust fund assets and default on benefit promises.

The aim of the study is to assess the impact of stringent lockdown policy on health and economic outcomes by constructing a stringency index at the State level in India. Results shows that stringent policy led to containment of spread as well as lowering of casualties related to COVID-19 across States. On economic front also, three-month moving average of the stringency index has a positive relationship with the growth in each of the economic indicators. Thus, study infers that India transformed the short-term trade-off between lives and livelihoods into a win-win in the medium to long-term that saves both lives and livelihoods.

In addition to altering drastically people's daily lives, COVID-19 has slowed down economic activities, imposed restrictions, and enforced lockdown, altogether causing significant effects on the environment. Studying the direction, magnitude, and durability of these effects carries a serious lesson for the whole world. Preliminarily evidence suggests that COVID-19 has temporarily improved air quality, reduced greenhouse gas emissions, and noise pollution, yet, due to lock-down, the flourishing of delivery services has pulled up the single-use plastics, increased the usages of vehicles by a lower ratio of passengers/vehicle, and raised demand for energy. Using the approach of the Ambiental Kuznets curve, we investigate the impact of COVID-19 total cases on the monthly average of carbon monoxide emissions measured in micrograms per cubic across a sample of developed, heavy polluters, economies from 2014 to 2020. Driscoll-Kraay regressions confirm the COVID-19 long-run polluting impact by increasing monoxide emissions in countries of analysis.

Today humankind lives-in world of business-mentality with technology-driven-lifestyles. In this progression, services are carried-out in competitive & rationality manner in global-economy country-wise such as Bangladesh-economy. While this was going on in human-society, most countries were hit by COVID-19 & consequences that raise question: what is COVID-19? The COVID-19 pandemic is an infectious disease that was initially detected in December of 2019 in Wuhan City, China. Since then, it has been spreading country-wise in mild to severity scales where Bangladesh, like most countries, was not prepared meeting the challenges. Addressing the crisis, Bangladesh, like many countries, started with the philosophy of traditional approaches. In the 1st-wave of COVID-19, government implemented lockdown laws and enforced to have-on-mask and maintain-physical-distance. However, no government effort was seen enforcing modern-approach for convincing people, making their way understanding the consequences & preventive-measures, thus people can take own decision facing the perceived-risk.

The elections on 4 April 2021 are of pivotal importance for Bulgaria’s rule of law. Borissov has built a framework for abusing the COVID-19 challenges for political benefits, so if he remains in power, he will surely take advantage of it.

PurposeThe paper examines the impact of COVID-19 on bank stock returns over various time scales and frequencies for 36 countries. Moreover, the authors look at the governments' responses to the corona crisis and examine its impact on bank stock returns.Design/methodology/approachThe paper applies continuous wavelet transformation to obtain robust estimates of the co-movement (coherency) between confirmed cases and bank stock returns over time and at different time scales. Furthermore, the authors apply fixed effects panel regression to examine the response of bank stocks to domestic COVID-19 policies.FindingsThe results indicate that the number of confirmed COVID-19 cases negatively impacts bank stock returns during different waves of the pandemic in the medium-run. However, there is only little dependence in the very short-run. Moreover, bank stock returns positively react to domestic COVID-19 polices. This demonstrates that governmental interventions not only reduce the spread of COVID-19 but are also able to thereby calm financial markets.Originality/valueThe application of wavelet methods to the field of economics and finance is relatively recent and allows the distinction between short-term and long-term effects. Standard econometric methods, in contrast, only operate within the time domain. This paper combines wavelet methods with conventional econometrics to answer the research question.

Since March 2020, numerous models have been developed to support policymakers in understanding, forecasting, and controlling the COVID-19 pandemic. Differences in data, assumptions, and underlying theory, coupled with unknowns about a novel virus, led these models to generate divergent forecasts and proposed responses. A policymaker using a single model is left to wonder if their decision is truly of high quality or if they are being misled by the idiosyncrasies of the selected model. In addition, many COVID-19 optimization models are cast as optimal control problems with abstract decision variables and frequent changes to policy, so translating the optimal solution to implementable actions is not straightforward. We propose a multi-model optimization (MMO) framework that identifies policies that perform well across structurally distinct models, and we apply this to design 12-month COVID-19 containment strategies. Our approach differs from the existing literature in two important aspects. First, we optimize using multiple state-of-the-art forecasting models currently in use. Second, we intentionally draw feasible intervention levels from each state’s own past and current responses, making it easy to implement the proposed policy. We find that a policy based on a single model can perform badly (cost increases of 100% or more) when models are misspecified, and that the MMO policy significantly diminishes the impact of model uncertainties. We propose optimal containment policies for all 50 US states over a one-year period and find that the optimal policy can vary significantly by state. We also study the impacts of virus variants and lockdown fatigue.

The present study investigates the degree of market responses through the scope of investors’ sentiment during the COVID-19 pandemic across G20 markets, by constructing a novel positive search volume index for COVID-19 (COVID19+). Our key findings, obtained using a Panel-GARCH model, indicate that an increased COVID19+ index suggests that investors decrease their COVID-19 related crisis sentiment by escalating their Google searches for positively associated COVID-19 related keywords. Specifically, we explore the predictive power of the newly constructed index on stock returns and volatility. According to our findings, investor sentiment positively (negatively) predicts the stock return (volatility) during the COVID-19. This is the first study of its kind assessing global sentiment by proposing a novel proxy and its impacts on the G20 equity market.

Patents are often seen as legal monopolies that impose exorbitant pricing, particularly for innovations in the pharmaceutical and medical device sectors where the inventions are usually protected through the patents. Many have argued that patent protection should not be extended to sectors where the public interest in obtaining those products or services is important, such as health-related products, which also includes the medications. This paper provides an overview of different policy choices that national governments might employ to both promote innovation in health-related goods and ensure low consumer prices for the basic medicines. Special consideration is given to the current COVID-19 health emergency and highlights the solutions through the published reviews to make the basic medications freely accessible to all countries.

This paper aims to provide an empirical investigation of the climate-growth joint dynamics considering exogenous shocks such as the COVID-19 in the Middle East and North Africa (MENA) region, with no presumptions imposed on the direction of causality. To quantify climate change, we use temperature and precipitation deviations from their historical trends. We estimate country- specific and panel vector autoregressive models with exogenous variables to supplement existing attempts that integrate climate change in economic mod- elling. Our findings lend evidence to the interrelation between climate change and economic growth without ruling out bi-directional causality, a reality that many climate studies tend to overlook.