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Register a new userStocks Vote with Their Feet: Can a Piece of Paper Document Fights the COVID-19 Pandemic?
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Assessing the trend of the COVID-19 pandemic and policy effectiveness is essential for both policymakers and stock investors, but challenging because the crisis has unfolded with extreme speed and the previous index was not suitable for measuring policy effectiveness for COVID-19. This paper builds an index of policy effectiveness on fighting COVID-19 pandemic, whose building method is similar to the index of Policy Uncertainty, based on province-level paper documents released in China from Jan.1st to Apr.16th of 2020. This paper also studies the relationships among COVID-19 daily confirmed cases, stock market volatility, and document-based policy effectiveness in China. This paper uses the DCC-GARCH model to fit conditional covariances change rule of multi-series. This paper finally tests four hypotheses, about the time-space difference of policy effectiveness and its overflow effect both on the COVID-19 pandemic and stock market. Through the inner interaction of this triad structure, we can bring forward more specific and scientific suggestions to maintain stability in the stock market at such exceptional times.
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Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.
Context. As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective. This study investigates the effects of the pandemic on developers wellbeing and productivity. Method. A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results. The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions. To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees home offices. Women, parents and disabled persons may require extra support.
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.
Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R_0 multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.
During its history, the ultimate goal of economics has been to develop similar frameworks for modeling economic behavior as invented in physics. This has not been successful, however, and current state of the process is the neoclassical framework that bases on static optimization. By using a static framework, however, we cannot model and forecast the time paths of economic quantities because for a growing firm or a firm going into bankruptcy, a positive profit maximizing flow of production does not exist. Due to these problems, we present a dynamic theory for the production of a profit-seeking firm where the adjustment may be stable or unstable. This is important, currently, because we should be able to forecast the possible future bankruptcies of firms due to the Covid-19 pandemic. By using the model, we can solve the time moment of bankruptcy of a firm as a function of several parameters. The proposed model is mathematically identical with Newtonian model of a particle moving in a resisting medium, and so the model explains the reasons that stop the motion too. The frameworks for modeling dynamic events in physics are thus applicable in economics, and we give reasons why physics is more important for the development of economics than pure mathematics. (JEL D21, O12) Keywords: Limitations of neoclassical framework, Dynamics of production, Economic force, Connections between economics and physics.
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