The spread of new coronavirus (COVID-19) infections continues to increase. The practice of social distance attracts attention as a measure to prevent the spread of infection, but it is difficult for some occupations. Therefore, in previous studies, the scale of factors that determine social distance has been developed. However, it was not clear how to select the items among them, and it seemed to be somewhat arbitrary. In response to this trend, this paper extracted eight scales by performing exploratory factor analysis based on certain rules while eliminating arbitrariness as much as possible. They were Adverse Conditions, Leadership, Information Processing, Response to Aggression, Mechanical Movement, Autonomy, Communication with the Outside, and Horizontal Teamwork. Of these, Adverse Conditions, Response to Aggression, and Horizontal Teamwork had a positive correlation with Physical Proximity, and Information Processing, Mechanical Movement, Autonomy, and Communication with the Outside had a negative correlation with Physical Proximity. Furthermore, as a result of multiple regression analysis, it was shown that Response to Aggression, not the mere teamwork assumed in previous studies, had the greatest influence on Physical Proximity.

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the predicted probability of employment, while in the student assignment context it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families, students) based on their preferences, but subject to meeting the planner's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner's threshold.

Miles et al. [1] recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19, focussing on the case of the U.K. They argue that the March-June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY). Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September. I note here two key problems that certainly significantly affect their estimates and cast doubt on their conclusions. Firstly, they cut off their calculations arbitrarily after 13 weeks, without costing the epidemic state at the end of the period. That is, they assume that we should be indifferent between mid-September states of 13 deaths a week and 7500 deaths a week, and corresponding infection rates. This seems indefensible unless one assumes that (a) there will be no future vaccine and no future improvements in treatment or in non-medical interventions, (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached. Even under these assumptions it is very questionable. Secondly, they ignore the costs of serious illness, possible long-term lowering of life quality, and possible lowering of life expectancy for COVID-19 survivors. These are uncertain, but clearly not negligible, and plausibly comparable to or larger than the costs in lives lost.

Corporate bankruptcy impacts the functioning of the economy as it impacts its various stakeholders: Shareholders, financial and operational lenders, and the government. This paper aims to study the impact of a wide array of profitability, leverage and efficiency ratios to predict early signs of bankruptcy in public listed companies in India using a logistic regression considering impacts at two levels: one year and two years before the filing of bankruptcy with the NCLT during the year 2019. The study proves that the accuracies of the classification model are 81.4% and 85.1% respectively for one year and two years before the bankruptcy.

Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect.

This paper analyzes a class of infinite-time-horizon stochastic games with singular controls motivated from the partially reversible problem. It provides an explicit solution for the mean-field game (MFG) and presents sensitivity analysis to compare the solution for the MFG with that for the single-agent control problem. It shows that in the MFG, model parameters not only affect the optimal strategies as in the single-agent case, but also influence the equilibrium price. It then establishes that the solution to the MFG is an $\epsilon$-Nash Equilibrium to the corresponding $N$-player game, with $\epsilon=O\left(\frac{1}{\sqrt N}\right)$.

Technological change is essential for balancing economic growth and environmental sustainability. This study measures and documents energy-saving technological change to understand its trends in advanced countries over recent decades. We estimate aggregate production functions with factor-augmenting technology using cross-country panel data and shift-share instruments, thereby measuring and documenting energy-saving technological change. Our results show how energy-saving technological change varies across countries over time and the extent to which it contributes to economic growth in 12 OECD countries from the years 1978 to 2005.

We study a novel economic network (supply chain) comprised of wire transfers (electronic payment transactions) among the universe of firms in Brazil (6.2 million firms). We construct a directed and weighted network in which vertices represent cities and edges connote pairwise economic dependence between cities. Cities (vertices) represent the collection of all firms in that location and links denote intercity wire transfers. We find a high degree of economic integration among cities in the trade network, which is consistent with the high degree of specialization found across Brazilian cities. We are able to identify which cities have a dominant role in the entire supply chain process using centrality network measures. We find that the trade network has a disassortative mixing pattern, which is consistent with the power-law shape of the firm size distribution in Brazil. After the Brazilian recession in 2014, we find that the disassortativity becomes even stronger as a result of the death of many small firms and the consequent concentration of economic flows on large firms. Our results suggest that recessions have a large impact on the trade network with meaningful and heterogeneous economic consequences across municipalities. We run econometric exercises and find that courts efficiency plays a dual role. From the customer perspective, it plays an important role in reducing contractual frictions as it increases economic transactions between different cities. From the supplier perspective, cities that are central suppliers to the supply chain seem to use courts inefficiency as a lawsuit barrier from their customers.

We study the optimal investment-consumption problem for a member of defined contribution plan during the decumulation phase. For a fixed annuitization time, to achieve higher final annuity, we consider a variable consumption rate. Moreover, to have a minimum guarantee for the final annuity, a safety level for the wealth process is considered. To solve the stochastic optimal control problem via dynamic programming, we obtain a Hamilton-Jacobi-Bellman (HJB) equation on a bounded domain. The existence and uniqueness of classical solutions are proved through the dual transformation. We apply the finite difference method to find numerical approximations of the solution of the HJB equation. Finally, the simulation results for the optimal investment-consumption strategies, optimal wealth process and the final annuity for different admissible ranges of consumption are given. Furthermore, by taking into account the market present value of the cash flows before and after the annuitization, we compare the outcomes of different scenarios.