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In this paper, we investigate how the COVID-19 pandemics and more precisely the lockdown of a sector of the economy may have changed our habits and, there-fore, altered the demand of some goods even after the re-opening. In a two-sector infinite horizon economy, we show that the demand of the goods produced by the sector closed during the lockdown could shrink or expand with respect to their pre-pandemic level depending on the length of the lockdown and the relative strength of the satiation effect and the substitutability effect. We also provide conditions under which this sector could remain inactive even after the lockdown as well as an insight on the policy which should be adopted to avoid this outcome.
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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.
Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the current pandemic started, when these goods were in shortage in the traditional economy. Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available, and by considering the wider impact of the pandemic on DWMs. First, we focus on vaccines. We find 250 listings offering approved vaccines, like Pfizer/BioNTech and AstraZeneca, as well as vendors offering fabricated proofs of vaccination and COVID-19 passports. Second, we consider COVID-19 related products. We reveal that, as the regular economy has become able to satisfy the demand of these goods, DWMs have decreased their offer. Third, we analyse the profile of vendors of COVID-19 related products and vaccines. We find that most of them are specialized in a single type of listings and are willing to ship worldwide. Finally, we consider a broader set of listings simply mentioning COVID-19. Among 10,330 such listings, we show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020. We anticipate that our effort is of interest to researchers, practitioners, and law enforcement agencies focused on the study and safeguard of public health.
We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiting the spread of COVID-19. We analysed data extracted from smartphone and wearable devices and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the UK, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post-hoc Dunns tests to assess differences in these features among baseline, pre-, and during-lockdown periods. We also studied behavioural differences by age, gender, body mass index (BMI), and educational background. We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between pre- and during-lockdown periods. We saw reduced sociality as measured through mobility features, and increased virtual sociality through phone usage. People were more active on their phones, spending more time using social media apps, particularly around major news events. Furthermore, participants had lower heart rate, went to bed later, and slept more. We also found that young people had longer homestay than older people during lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. RADAR-base can be used to rapidly quantify and provide a holistic view of behavioural changes in response to public health interventions as a result of infectious outbreaks such as COVID-19.
We provide quantitative predictions of first order supply and demand shocks for the U.S. economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 22% of the US economys GDP, jeopardise 24% of jobs and reduce total wage income by 17%. At the industry level, sectors such as transport are likely to have output constrained by demand shocks, while sectors relating to manufacturing, mining and services are more likely to be constrained by supply shocks. Entertainment, restaurants and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks -- we expect them to be substantially amplified by feedback effects in the production network.
What is the impact of COVID-19 on South Africa? This paper envisages assisting researchers and decision-makers in battling the COVID-19 pandemic focusing on South Africa. This paper focuses on the spread of the disease by applying heatmap retrieval of hotspot areas and spatial analysis is carried out using the Moran index. For capturing spatial autocorrelation between the provinces of South Africa, the adjacent, as well as the geographical distance measures, are used as a weight matrix for both absolute and relative counts. Furthermore, generalized logistic growth curve modeling is used for the prediction of the COVID-19 spread. We expect this data-driven modeling to provide some insights into hotspot identification and timeous action controlling the spread of the virus.
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