Multistate Models for the Recovery Process in the Covid-19 Context: An Empirical Study of Chinese Enterprises

Abstract

The Covid-19 pandemic has severely affected enterprises worldwide. It is thus of practical significance to study the process of enterprise recovery from Covid-19. However, the research on the effects of relevant determinants of business recovery is limited. This article presents a multistate modeling framework that considers the determinants, recovery time, and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic (recovery state), with the help of an accelerated failure time model. Empirical data from 750 enterprises were used to evaluate the recovery process. The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations. With the increase of supplies and orders, the probability of transition between different recovery states gradually increases, and the recovery time of enterprises becomes shorter. For manufacturing industries, the factors that hinder recovery are more complex. The main problems are employee panic and order cancellations in the initial stage, employee shortages in the middle stage, and raw material shortages in the full recovery stage. This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

“…We assume that the future evolution of an individual depends on the number of days in the present state (positive, hospital, ICU, post-ICU) but not on the days spent in previous states. In particular, we do not assume that the distributions of the times have a constant failure rate, which is a harsh condition needed for the model to be Markovian [ 37 ]. Indeed, as we use a nonparametric approach, we do not impose any restriction on the distribution of the random times T 1 , ….,T 5 ; also, no previous knowledge of these distributions is assumed, since the model will estimate the corresponding survival functions from the cohort data.…”

Assessing the Regional Economic Ripple Effect of Flood Disasters Based on a Spatial Computable General Equilibrium Model Considering Traffic Disruptions

“…Typically, disasters affect labor and capital endowments, thereby hindering firms' production process (Yang, Chen, et al 2022). We set the labor loss rate at 5.20% based on the entire province's labor productivity from the Statistical Bulletin of National Economic and Social Development of Hubei Province, 2020.…”

Pandemic publishing: A bibliometric review of COVID‐19 research in the crisis and disaster literature

“…Das et al ( 2021 ), for example, use a technique to measure people's vulnerability in relation to the COVID‐19 pandemic that, they argue, is also potentially valuable to measure vulnerability in relation to climate change. Yang et al ( 2022a ) combine the two aspects of risk into one study when they discuss how risk perceptions, too, differ starkly among communities. They show, for example, that people express their panic more strongly in areas surrounding an outbreak, and in economically developed areas.…”

“…Specifically, many studies within this theme focus on how the pandemic impacts the performance of businesses—hence this theme's topic “performance.” Fasth et al ( 2022 ), for example, study the crisis management practices of business leaders through 1000 interviews in Swedish small‐ and medium‐sized enterprises. Others, such as Yang et al ( 2022a ) develop a framework for businesses recovery from the pandemic impact, which explains why “framework” is also a key topic of this theme. Resilience mostly refers to the performance of those businesses that “bounce back” quickly and manage to recover from the impact of the pandemic strongly (Katare et al, 2021 ).…”