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This study aims to examine the relationship between the duration (in days) of states’ shelter-in-place orders; state demographic characteristics; and the rates of spread (cases), death (mortality), and recovery of COVID-19 in the USA.

State-level data across 50 states and Washington D.C. from January 23, 2020, to June 11, 2020, and a multivariate regression analysis were used to empirically investigate the impacts of the duration of shelter-in-place orders and state demographic characteristics on the rates of cases, mortality and recovery per capita of COVID-19.

This study finds that a longer duration of a shelter-in-place order is associated with lower cases and deaths per capita from COVID-19. This study also finds that demographic characteristics, such as the percentage of people who are unsheltered homeless, family size, percentage of individuals with health insurance, income inequality, unemployment rate, gender and race, are related to cases, mortality and recovery rates of COVID-19.

This study offers policy implications for state and locality (e.g., city, region and country) lockdown decisions and salient demographics to consider curbing the spread and mortality rate of the COVID-19 pandemic. Study results are important to consider as the world braces for the anticipated resurgence of COVID-19.

This study reveals that the duration of shelter-in-place orders and demographics in states are related to the rates of spread, mortality, and recovery of COVID-19.

Employment rates fell dramatically between March and April 2020 as the initial shock of the COVID-19 pandemic reverberated through the US labor market. This paper uses data from the CPS Basic Monthly Files to document that the employment decline was particularly severe for immigrants. Historically, immigrant men are more likely to work than native men. The pandemic-related labor market shock eliminated the immigrant employment advantage. After this initial precipitous drop, however, the employment recovery through June 2021 was much stronger for immigrants, and particularly for undocumented immigrants. The steep drop in immigrant employment at the start of the pandemic occurred partly because immigrants were less likely to work in jobs that could be performed remotely and suffered disproportionate employment losses as only workers with remotable skills were able to continue working from home. The stronger employment recovery of undocumented immigrants, relative to that experienced by natives or legal immigrants, is mostly explained by the fact that undocumented workers were not eligible for the generous unemployment insurance (UI) benefits offered to workers during the pandemic.

Despite recent advances, neither organizational studies nor the scholarship on economic resilience has systematically addressed how the ecologies of organizations that populate local economies can serve as infrastructures for responding proactively to economic shocks. Using county-level data, this study analyzes relationships between the prevalence of organizational alternatives to shareholder value-oriented (SVO) corporations within a particular locality and its unemployment levels during and after the Great Recession. The results support the hypothesis that the presence of such alternative organizations can enhance the capacities of local economies to resist and recover from recession shocks. Cooperative, municipal, and community-based enterprises, research universities, and nonprofits more generally were associated with greater resistance to the recession shock and stronger recoveries – specifically, lower surges in unemployment rates from 2007 to 2010 and greater reductions in unemployment rates from 2010 to 2016. By contrast, SVO corporations were associated with greater surges in unemployment and perhaps weaker recoveries. Providing a proof of concept, this study opens up new lines of inquiry for organizational studies by linking organizational ecologies to the promotion of collective efficacy and a more broadly shared prosperity in economic life.

Credit derivatives continue to grow in popularity as well as complexity. While single‐name credit default swaps are still the most popular instruments, second‐generation products have become more commonplace. Second generation products are those whose payoffs are contingent on the viability of a number of firms and include instruments such as default baskets and synthetic collateralized debt obligations. The purpose of this paper is to provide a transparent and detailed account of default basket valuation along with thorough and intuitive explanations of comparative statics and the relationship between basket values and default correlation.

The paper delineates the standard approach to valuing default baskets and with its implementation examines results for two copula functions and the input assumptions which are critical to the valuation process.

It is found that the assumptions are critical to the valuation and that the copula chosen also has an impact on pricing and comparative statics.

This paper is very practical in its orientation and takes a pedagogical approach in its explanation of default baskets, the standard model, and key assumptions.

This paper fills a gap in the literature as prior works are more focused on certain enhancements or nuances of modeling basket credit derivatives while this work centers on the standard model and provides a thorough analysis and explanation of the comparative statics as well as a discussion of model limitations. This paper is ideal reading for those that seek an understanding of the modeling and risks associated with multi‐name credit derivatives.

This paper aims to examine the impact of vocational training on unemployed workers not typically studied: women enrolled in engineering or computer programming training and high school dropouts.

Using data from New Jersey's Individual Training Grant (ITG) program and a quasi‐experimental design, the study compares the ITG groups' re‐employment and wage recovery rates with a matched comparison group.

The article finds that women enrolled in the male‐dominated fields of engineering or computer programming experience re‐employment rates that are lower than or similar to those in the comparison group, but they experience higher wage recovery in 8th and 12th quarters after claiming unemployment insurance (UI). Hispanic high school dropouts experience both higher re‐employment and wage recovery rates than their comparison group, but the wage recovery advantage disappears when those enrolled in truck driving training are removed from the sample. Further, white and black high school dropouts experience no re‐employment or wage recovery advantage. For all participants, the study finds participants experience a higher re‐employment rate than the comparison group beginning in the fifth quarter and experience no wage recovery advantage.

To address the concern of selection bias, a difference‐in‐difference wage model controls for time‐variant differences in unobservables and an employment regression model controls for remaining differences in the matching variables.

These results suggest that training improves re‐employment chances and that type of training matters with respect to wage recovery. In this sample, those enrolled in truck driving training, engineering, and computer programming tended to experience higher wage recovery than their comparison group.

This paper examines the impact of vocational training on unemployed workers not typically studied.

Demonstrates a method for examining service failures and recovery strategies in service industries and provides a typology of service failures and recoveries in the restaurant industry. Based on 373 critical incidents collected from restaurant customers, uses the critical incident technique (CIT) to identify 11 unique failure types and eight different recovery strategies. Additional data regarding the magnitude of the service failure, the service recovery rating, the lapsed time since the failure/recovery incident, and customer retention rates were also collected. Presents this information along with managerial and research implications.

The purpose of this paper is to verify recovery risk management capabilities by lessors. It tests several hypotheses and finds out interesting specific results for lessors.

The approach is empirical: two different database of leasing contracts are analysed with econometric methodologies.

There is clear evidence that: lessors are ex ante able to balance the probability of default and the loss given default case by case, using proper contract structures; and they carefully manage recovery procedures and strategies according to operations' characteristics.

The data used are large enough, but come from institutions concentrated in Italy. Future research could be extended to other relevant countries.

Results presented are verified in leasing companies which made a limited use of rating systems and credit risk model: they have been achieved by the continuous improvements of traditional lending practices. The development of modern reliable systems can enhance risk management capabilities; our findings can help building more structured and advanced credit risk management tools.

The paper adds to the literature in the sense that gives clear evidence of a neglected but important fact of real world credit markets: financial intermediaries have the capability of properly assessing risk components and manage loss given default (LGD) in order to control overall credit risk.