Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis

The COVID-19 pandemic, which started at Wuhan, has shut down world economies, prompting governments to impose drastic lockdown measures of the economy and the society. As these measures are exhausted, non-COVID-19 related issues such as those associated with the mental and physical well-being of people under lockdowns became an emerging concern. As these issues are evident, not to mention the economic downturn, governments are currently looking at designing lockdown relaxation efforts by simultaneously considering both public health and economic restart. Without documented experiences to rely on, governments are resorting to trial-and-error approach in creating a lockdown exit strategy while preventing succeeding waves of cases that may overwhelm healthcare facilities. Thus, this work pioneers the use of the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy (IF) sets along with the domain of public health and the emerging COVID-19 pandemic. The DEMATEL handles the intertwined causal relationships among guideline protocols for the relaxation strategy. The intuitionistic fuzzy set theory addresses the vagueness and uncertainty of human judgments in the context of the DEMATEL. A case study of the Philippine government response for the lockdown exit is presented to evaluate the applicability of the proposed method. Findings reveal that compliance of minimum public health standards, limited movement of persons, suspension of physical classes, the prohibition of mass gatherings, non-operation of category IV industries, and non-operation of hotels or similar establishments are the most crucial protocols for such strategy. These findings offer practical insights for the government to allocate resources and impose measures to ensure their implementation, as well as for developing mitigation efforts to cushion their socio-economic impacts. Policy insights and avenues for future works are also discussed.     

The gloom of the COVID-19 shock in the hospitality industry: A study of consumer risk perception and adaptive belief in the dark cloud of a pandemic

As the new coronavirus (COVID-19) spreads globally, the hospitality industry is at the heart of implementing social distancing, a measure demonstrated to be effective in flattening the epidemic curve. Informed by the perceived risk theory, this research examines how the customer’s perception of the shock of the coronavirus pandemic impacts on their beliefs, and how their beliefs could influence their anticipated emotions (negative and positive) which could affect their future desire towards visiting restaurants. Structural equation modelling was used to understand the research constructs’ associations. This study provides two key suggestions: (i) that the hospitality industry is built on trust from their customers by supporting and resourcing consumers’ self-protection behaviour and adoptive belief, and (ii) that the economic influence and the continuous uncertainty and transformation of the restaurant business need the enhancement of localisation strategies, practices and performance.     

Vulnerability of financial markets in India: The contagious effect of COVID-19

This study aims to examine the impact of COVID-19 on financial markets, using emerging market data. Specifically, panel data regression is applied on 3200 observations for daily market returns during lockdown in India. The event study methodology is adopted to show abnormal returns registered in the lockdown period. A contrasting breakdown effect of COVID-19 on various Indian industries has been observed through sectoral analysis. The study also provides empirical evidence for lockdown measures taken by the government on stock market returns and post lockdown impact of COVID-19 on daily market returns for over 6550 observations.     

Revenge buying after the lockdown: Based on the SOR framework and TPB model

With the end of the pandemic and the lifting of the lockdown, the consumer market experienced revenge buying. The purpose of this study is to investigate the causes of revenge buying using the stimulus–organ–response (SOR) framework and the theory of planned behavior (TPB) model. Structural equation modeling was used to analyze data collected from 350 residents of Shanghai, China, after the city’s lockdown was lifted. The findings imply that perceived scarcity, perceived susceptibility, and social influence regarding the lockdown can stimulate individuals’ anxiety, inducing behavioral intentions and ultimately leading to revenge buying consumer behavior. Theoretically, this study provides a novel explanation of revenge buying behavior. Additionally, conclusions offer ramifications for management and implementation strategies for dealing with revenge buying after sudden disasters.     

Taming the spread of an epidemic by lockdown policies

We study the problem of a policymaker who aims at taming the spread of an epidemic while minimizing its associated social costs. The main feature of our model lies in the fact that the disease’s transmission rate is a diffusive stochastic process whose trend can be adjusted via costly confinement policies. We provide a complete theoretical analysis, as well as numerical experiments illustrating the structure of the optimal lockdown policy. In all our experiments the latter is characterized by three distinct periods: the epidemic is first let to freely evolve, then vigorously tamed, and finally a less stringent containment should be adopted. Moreover, the optimal containment policy is such that the product “reproduction number $\times$ percentage of susceptible” is kept after a certain date strictly below the critical level of one, although the reproduction number is let to oscillate above one in the last more relaxed phase of lockdown. Finally, an increase in the fluctuations of the transmission rate is shown to give rise to an earlier beginning of the optimal lockdown policy, which is also diluted over a longer period of time.     

Learning versus habit formation: Optimal timing of lockdown for disease containment

We analyze a model where the government has to decide whether to impose a lockdown in a country to prevent the spread of a possibly virulent disease. If the government decides to impose a lockdown, it has to determine its intensity, timing and duration. We find that there are two competing effects that push the decision in opposite directions. An early lockdown is beneficial not only to slow down the spread of the disease, but creates beneficial habit formation (such as social distancing, developing hygienic habits) that persists even after the lockdown is lifted. Against this benefit of an early lockdown, there is a cost from loss of information about the virulence and spread of the disease in the population in addition to a direct cost to the economy. Based on the prior probability of the disease being virulent, we characterize the timing, intensity and duration of a lockdown with the above mentioned tradeoffs. Specifically, we show that as the precision of learning goes up, a government tends to delay the imposition of lockdown. Conversely, if the habit formation parameter is very strong, a government is likely to impose an early lockdown.     

High-Frequency Changes in Shopping Behaviours,Promotions and the Measurement of Inflation: Evidence from the Great Lockdown*

We use real-time scanner data in Great Britain during the COVID-19 pandemic to investigate the drivers of the inflationary spike at the beginning of lockdown and to quantify the impact of high-frequency changes in shopping behaviours and promotions on inflation measurement. Although changes in product-level expenditure shares were unusually high during lockdown, we find that the induced bias in price indices that do not account for expenditure switching is not larger than in prior years. We also document substantial consumer switching towards online shopping and across retailers, but show this was not a key driver of the inflationary spike. In contrast, a reduction in price and quantity promotions was key to driving higher inflation, and lower use of promotions by low-income consumers explains why they experienced moderately lower inflation. Overall, changes in shopping behaviours played only a minor role in driving higher inflation during lockdown; higher prices were the main cause, in particular through a reduced frequency of promotions.     

Asset pricing during pandemic lockdown

This paper examines the implications of lockdown policies during early stages of pandemics for asset prices. We build a simple susceptible-infected-recovered model with microeconomic foundations, which allows us to obtain qualitative results with economic implications. In our model, lockdown policies reduce (i) labour income by decreasing working hours and (ii) precautionary savings by decreasing susceptible agents’ probability of getting infected in the future. We qualitatively show that strengthening lockdown measures negatively impacts asset prices at the time of implementation. Our empirical analysis using data from advanced countries supports this finding. Depending on parameter values, our numerical analysis displays a V-shaped recovery of asset prices and an L-shaped recession of consumption. The rapid recovery of asset prices occurs only if the lockdown policies are insufficiently stringent to reduce the number of new periodic cases. This finding implies the possibility that lenient lockdowns have contributed to rapid stock market recovery at the beginning of the COVID-19 pandemic.