Relative bed allocation for COVID-19 patients, EHR investments, and COVID-19 mortality outcomes.

Managing flexibility in the relative bed allocation for COVID-19 and non-COVID-19 patients was a key challenge for hospitals during the COVID-19 pandemic. Based on organizational information processing theory (OIPT), we propose that the local electronic health record (EHR) systems could improve patient outcomes through improved bed allocation in the local area. In an empirical analysis of county-level weekly hospital data in the US, relative capacity of beds in hospitals with higher EHR was associated with lower 7-, 14-, and 21-day forward-looking COVID-19 death rate at the county-level. Testing for cross-state variation in non-pharmaceutical interventions along contiguous county border-pair analysis to control for spatial correlation varying between state variations in non-pharmaceutical intervention policies, 2SLS analysis using quality ratings, and using foot-traffic data at the US hospitals our findings are generally supported. The findings have implications for policymakers and stakeholders of the local healthcare supply chains and EHR systems.

The response of household debt to COVID-19 using a neural networks VAR in OECD.

This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel VAR for OECD countries. Our model also includes a plethora of non-pharmaceutical and pharmaceutical interventions. We opt for a global neural networks panel VAR (GVAR) methodology that nests all OECD countries in the sample. Because linear factor models are unable to capture the variability in our data set, the use of an artificial neural network (ANN) method permits to capture this variability. The number of factors, as well as the number of intermediate layers, is determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. We also report δ-values that capture the dominance of each individual country in the network. In terms of dominant countries, the UK, the USA, and Japan dominate interconnections within the network, but also countries like Belgium, Netherlands, and Brazil. Results reveal that household debt positively responds to COVID-19 infections and deaths. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt, though they are of transitory nature. However, vaccinations and testing appear to negatively affect household debt.

Eco-efficiency estimation with quantile stochastic frontiers: Evidence from the United States.

This paper based on quantile stochastic frontier framework constructs quantile eco-efficiency measures. Using the estimates from the quantile stochastic frontier, the eco-performance of the U.S. states for nitrogen oxides (NOX), carbon dioxide (CO2), and sulfur dioxide (SO2) emissions is evaluated. A decoupling analysis involving the evaluation of the nonsynchronous change among states' economic output and environmental degradation levels is also performed. The findings suggest that U.S. states have followed a decoupling process among their GDP and emission levels over the period 1990-2017. In addition, a quantile eco-productivity change estimator is presented alongside with its main components (i.e. quantile eco-technical change and quantile eco-efficiency change). Our findings suggest that over the examined period states' eco-productivity levels have been improved driven both from their eco-technical and eco-efficiency change levels.

On identifying risk-adjusted efficiency gains or losses of prospective mergers and acquisitions.

We propose a new approach to evaluate and compare ex-ante the risk-adjusted efficiency gains or losses of potential mergers and acquisitions (M &A). We test our methodology in the banking sector by estimating a latent class stochastic frontier model to account for the unobserved heterogeneity. We show that post-prospective M &A financial institutions can be better equipped to withstand potential adverse economic conditions. We highlight that similarities in strategic characteristics are vital in the creation of post-consolidation cost efficiency surplus. Our results are consistent after various robustness tests. Our findings have important policy implications in light of the challenges the traditional banking business model faces in the current digitalisation era.

Understanding the COVID-19 tourist psyche: The Evolutionary Tourism Paradigm.

Studies across the social sciences are making increasing use of an evolutionary perspective. Yet, despite its potential, the application of evolutionary psychology in tourism research is scant. Evolutionary psychology is arguably one of the most useful approaches to understanding the effects of the Coronavirus pandemic on the tourist's psyche. This research highlights, explains, and empirically demonstrates the vast untapped potential of this perspective for post-COVID-19 tourism research. The authors develop an Evolutionary Tourism Paradigm, which is based on biological epistemology and theory to address questions in post-COVID-19 tourism research. This paradigm is brought to life through a developed ocean and islands model, and its utility for future research endeavors on the Coronavirus pandemic is empirically demonstrated in two studies.