In the first half of 2020, several countries have responded to the challenges posed by the Covid-19 pandemic by restricting their export of medical supplies. Such measures are meant to increase the domestic availability of critical goods, and are commonly used in times of crisis. Yet, not much is known about their impact, especially on countries imposing them. Here we show that export bans are, by and large, counterproductive. Using a model of shock diffusion through the network of international trade, we simulate the impact of restrictions under different scenarios. We observe that while they would be beneficial to a country implementing them in isolation, their generalized use makes most countries worse off relative to a no-ban scenario. As a corollary, we estimate that prices increase in many countries imposing the restrictions. We also find that the cost of restraining from export bans is small, even when others continue to implement them. Finally, we document a change in countries' position within the international trade network, suggesting that export bans have geopolitical implications.

To investigate a time-consistent optimal strategy for the continuous time mean-variance model, we develop a new method to establish the Bellman principle. Based on this new method, we obtain a time-consistent dynamic optimal strategy that differs from the pre-committed and game-theoretic strategies. A comparison with the existing results on the continuous time mean-variance model shows that our method has several advantages. The explicit solutions of the dynamic optimal strategy and optimal wealth are given. When the dynamic optimal strategy is given at the initial time, we do not change it in the following investment time interval.

This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. USDT and TUSD are consistent outliers with respect to their returns, while HOT, NEXO, MKR and XEM are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis.

This article uses data of subjective Life Satisfaction aggregated to the community level in Canada and examines the spatial interdependencies and spatial spillovers of community happiness. A theoretical model of utility is presented. Using spatial econometric techniques, we find that the utility of community, proxied by subjective measures of life satisfaction, is affected both by the utility of neighbouring communities as well as by the latter's average household income and unemployment rate. Shared cultural traits and institutions may justify such spillovers. The results are robust to the different binary contiguity spatial weights matrices used and to the various econometric models. Clusters of both high-high and low-low in Life Satisfaction communities are also found based on the Moran's I test

What are the chances of an ethical individual rising through the ranks of a political party or a corporation in the presence of unethical peers? To answer this question, I consider a four-player two-stage elimination tournament, in which players are partitioned into those willing to be involved in sabotage behavior and those who are not. I show that, under certain conditions, the latter are more likely to win the tournament.

Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections with 15 other facilities. Controlling for demographic and other factors, a home's staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Results suggest that eliminating staff linkages between nursing homes could reduce COVID-19 infections in nursing homes by 44 percent.

Public transit disruption is becoming more common across different transit services, which can have a destructive influence on the resiliency and reliability of the transportation system. Utilizing a recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to unplanned service disruption and disclose the factors that affect their behavior.

Developments in the distributed ledger technology have led to new types of assets with a broad range of purposes. Although some classification frameworks for common instruments from traditional finance and some for these new, so called cryptographic assets already exist and are used, a holistic approach to integrate both worlds is missing. The present paper fills this research gap by identifying 14 attributes, each of which is assigned different characteristics, that can be used to classify all types of assets in a structured manner. Our proposed taxonomy which is an extension of existing classification frameworks, summarises these findings in a morphological box and is tested for practicability by classifying exemplary assets like cash and bitcoin. The final classification framework can help to ensure that the various stakeholders, such as investors or supervisors, have a consistent view of the different types of assets, and in particular of their characteristics, and also helps to establish standardised terminology.

We consider a game-theoretic model of a market where investors compete for payoffs yielded by several assets. The main result consists in a proof of the existence and uniqueness of a strategy, called relative growth optimal, such that the logarithm of the share of its wealth in the total wealth of the market is a submartingale for any strategies of the other investors. It is also shown that this strategy is asymptotically optimal in the sense that it achieves the maximal capital growth rate when compared to competing strategies. Based on the results obtained, we study the asymptotic structure of the market when all the investors use the relative growth optimal strategy.

We establish a Nash equilibrium in a market with $ N $ agents with the performance criteria of relative wealth level when the market return is unobservable. Each investor has a random prior belief on the return rate of the risky asset. The investors can be heterogeneous in both the mean and variance of the prior. By a separation result and a martingale argument, we show that the optimal investment strategy under a stochastic return rate model can be characterized by a fully-coupled linear FBSDE. Two sets of deep neural networks are used for the numerical computation to first find each investor's estimate of the mean return rate and then solve the FBSDEs. We establish the existence and uniqueness result for the class of FBSDEs with stochastic coefficients and solve the utility game under partial information using deep neural network function approximators. We demonstrate the efficiency and accuracy by a base-case comparison with the solution from the finite difference scheme in the linear case and apply the algorithm to the general case of nonlinear hidden variable process. Simulations of investment strategies show a herd effect that investors trade more aggressively under relativeness concerns. Statistical properties of the investment strategies and the portfolio performance, including the Sharpe ratios and the Variance Risk ratios (VRRs) are examed. We observe that the agent with the most accurate prior estimate is likely to lead the herd, and the effect of competition on heterogeneous agents varies more with market characteristics compared to the homogeneous case.

An important theme is how to maximize the cooperation of employees when dealing with crisis measures taken by the company. Therefore, to find out what kind of employees have cooperated with the company's measures in the current corona (COVID-19) crisis, and what effect the cooperation has had to these employees/companies to get hints for preparing for the next crisis, the pass analysis was carried out using awareness data obtained from a questionnaire survey conducted on 2,973 employees of Japanese companies in China. The results showed that employees with higher social capital and resilience were more supportive of the company's measures against corona and that employees who were more supportive of corona measures were less likely to leave their jobs. However, regarding fatigue and anxiety about the corona felt by employees, it was shown that it not only works to support cooperation in corona countermeasures but also enhances the turnover intention. This means that just by raising the anxiety of employees, even if a company achieves the short-term goal of having them cooperate with the company's countermeasures against corona, it may not reach the longer-term goal by making them increase their intention to leave. It is important for employees to be aware of the crisis and to fear it properly. But more than that, it should be possible for the company to help employees stay resilient, build good relationships with them, and increase their social capital to make them support crisis measurement of the company most effectively while keeping their turnover intention low.

This paper aims to estimate the impact of economic and financial crises on the unemployment rate in the European Union, taking also into consideration the institutional specificities, since unemployment was the main channel through which the economic and financial crisis influenced the social developments.. In this context, I performed two institutional clusters depending on their inclusive or extractive institutional features and, in each cases, I computed the crisis effect on unemployment rate over the 2003-2017 period. Both models were estimated by using Panel Estimated Generalized Least Squares method, and are weighted by Period SUR option in order to remove, in advance the possible inconveniences of the models. The institutions proved to be a relevant criterion that drives the impact of economic and financial crises on the unemployment rate, highlighting that countries with inclusive institutions are less vulnerable to economic shocks and are more resilient than countries with extractive institutions. The quality of institutions was also found to have a significant effect on the response of unemployment rate to the dynamic of its drivers.

In this paper, we provide a philosophical account of the value of creative systems for individuals and society. We characterize creativity in very broad philosophical terms, encompassing natural, existential, and social creative processes, such as natural evolution and entrepreneurship, and explain why creativity understood in this way is instrumental for advancing human well-being in the long term. We then explain why current mainstream AI tends to be anti-creative, which means that there are moral costs of employing this type of AI in human endeavors, although computational systems that involve creativity are on the rise. In conclusion, there is an argument for ethics to be more hospitable to creativity-enabling AI, which can also be in a trade-off with other values promoted in AI ethics, such as its explainability and accuracy.

Since 1986, Government of Botswana has been running an Agricultural Credit Guarantee Scheme for dry-land arable farming. The scheme purports to assist dry-land crop farmers who have taken loans with participating banks or lending institutions to help them meet their debt obligations in case of crop failure due to drought, floods, frost or hailstorm. Nonetheless, to date, the scheme has focused solely on drought. The scheme has placed an unsustainable financial burden on Government because it is not based on actuarially sound principles. This paper argues that the level of Government subsidies should take into account the gains made by farmers during non-drought years. It recommends a quasi self-financing mechanism that takes into account non-specific risks.