How consumer behaviours changed in response to COVID-19 lockdown stringency measures: A case study of Walmart.

Walmart is a major player in the US retail sector and was one of the grocery corporations that bucked the trend of declining retail sales at the start of the COVID-19 pandemic in 2020. Particularly in the initial stages of the pandemic, governance priorities focussed on restricting the movement of people and closing non-essential retailers and service providers to slow the spread of the virus and keep people safe. This paper investigates the impact of non-pharmaceutical interventions, in the form of lockdown stringency measures, on consumer purchasing behaviours for essential goods over the onset of the pandemic. Focussing on both instore and online sales outcomes for Walmart in the US, we examine changes between pre-pandemic trends in two different sales outcomes, sales transactions and total spend, and trends in 2020. We then employ a series of multi-level regression models to estimate the impact that imposed stringency measures had on these sales outcomes, at both national and state level. Results indicate that nationally consumers were making fewer, larger physical shopping trips and huge increases in online sales was seen ubiquitously across the country. Novel and expansive insights from such a wide-spread retailer, such as Walmart, can help retailers, stakeholders and policy makers understand changing consumption trends to inform business strategies and resilience planning for the future. Furthermore, this study highlighted the value of examining spatial trends in sales outcomes and hopes to influence greater consideration of this in future research.

Cui bono? Large-scale evidence on the impact of COVID-19 policy measures on listed firms.

By using a large international firm-level data set this paper aims to make a contribution in better understanding how COVID-19 related stringency and economic support measures actually affected the corporate sector. Our most important findings can be summarized as follows: First, we find robust evidence that stringency measures had a statistically and economically significant positive impact on listed firms. Second, with respect to the effects of economic support measures the evidence seems, at best, to be weakly in favor of a positive impact. Third, small and employment intensive companies profited most from economic support measures. Fourth, also highly leveraged or even Zombie firms profited more from these support measures than others. Overall, the results are in line with official policies aimed at sheltering SMEs and human capital intensive firms from the Corona shock waves. However, it also seems that governments unintentionally supported firms in financial difficulties or with non-viable business models already before the pandemic.

The great lockdown, uncertainty and trade: asymmetric impact on imports.

The world economy has recently navigated through the pandemic caused by the coronavirus. Almost all the affected countries have responded with stringency measures to control the pandemic. However, these restrictions appear to have critically impacted the global supply chain and cross-border movement of goods. In this regard, we attempt to investigate the impact of pandemic-related stringency measures on India's import demand. For this purpose, we use bilateral monthly import data of India with its major trading counterparts. Our findings suggest that stringency measures have a positive impact on imports, indicating that the economy relies more on imported items when its domestic production and supply chain are disrupted by the pandemic-related restrictions. Conversely, the import origin countries' restrictions have a negative impact on Indian imports, indicating that these restrictions have adversely affected the production and supply chain in origin countries, thereby reducing the overall flow of imports to India. We also find that economic policy uncertainty of home and product origin countries has a negative impact on Indian imports. Our results also confirm that the pandemic-related restrictions and different types of uncertainty have an asymmetric effect on imports.

Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis.

Background: On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot. Methods: In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis. Results: Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population. Conclusion: Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.

Carrot and stick: Economic support and stringency policies in response to COVID-19.

To address the economic losses caused by the COVID-19 pandemic, countries have implemented, together with policies aimed at stopping the spread of the virus, a mixture of fiscal and monetary measures. This work investigates the effect of containment policies and economic support measures on economic growth in the short run, investigating a time window of six quarters in a cross country perspective. Our results confirm the existence of a negative effect of stringency measures on GDP; we also detect a positive effect from economic support measures. Moreover, looking at the interaction between these two kinds of interventions, our findings suggest that up to a relatively low level of stringency policies, economic support measures are able to positively counterbalance the negative impact of containment and closure policies. When the level of closures became more severe, however, the economic support measures that countries adopt are not able to completely recoup, in the short run, the economic losses due to stringency policies. Results suggest that in order to have a positive net effect, policymakers should take into account the level of stringency measures implemented before investing in economic support.

Death takes no bribes: Impact of perceived corruption on the effectiveness of non-pharmaceutical interventions at combating COVID-19.

Corruption is considered in the literature as an activity with several externalities and spillover effects. Adding to the recent research on the corruption-COVID-19 nexus, we study the impact of corruption on coronavirus cases. High perceived levels of corruption have been proven to lead to lower institutional trust, and hence possibly to lower levels of citizen compliance with non-pharmaceutical interventions (NPIs), such as lockdowns, imposed by the authorities during the first wave of the pandemic to reduce the spread of coronavirus. Applying quantitative analysis with the use of hybrid models, we find that in countries with higher levels of perceived corruption, across alternative corruption measures, more COVID-19 cases are observed, ceteris paribus. This suggests that corruption has a detrimental effect on the spread of COVID-19, and that countries experiencing higher levels of corruption should pay extra attention when implementing NPIs.

Mobility and COVID-19 mortality across Scandinavia: A modeling study.

BACKGROUND: In response to COVID-19, the Swedish government imposed few travel and mobility restrictions. This contrasted with its Scandinavian neighbours which implemented stringent restrictions. The influence these different approaches had on mobility, and thus on COVID-19 mortality was investigated. METHODS: Datasets indicating restriction severity and community mobility were examined; Google's 'Community Movement Reports' (CMR) show activity at key location categories; the Oxford COVID-19 Government Response Tracker collates legislative restrictions into a 'Stringency Index' (SI). RESULTS: CMR mobility categories were negatively correlated with COVID-19 mortality. The strongest correlations were obtained by negatively time lagging mortality data, suggesting restrictions had a delayed influence. During the 'first wave' a model using SI (AIC 632.87) proved favorable to one using contemporaneous CMR data and SI (AIC 1193.84), or lagged CMR data and SI (AIC 642.35). Validation using 'second wave' data confirmed this; the model using SI solely again being optimal (RMSE: 0.2486 vs. 0.522 and 104.62). Cross-country differences were apparent in all models; Swedish data, independent of SI and CMR, proved significant throughout. There was a significant association for Sweden and the death number across models. CONCLUSION: SI may provide a broader, more accurate, representation of changes in movement in response to COVID-19 restrictions.

A Study of the Association between the Stringency of Covid-19 Government Measures and Depression in Older Adults across Europe and Israel.

Background: The COVID-19 pandemic is having major adverse consequences for the mental health of individuals worldwide. Alongside the direct impact of the virus on individuals, government responses to tackling its spread, such as quarantine, lockdown, and physical distancing measures, have been found to have a profound impact on mental health. This is manifested in an increased prevalence of anxiety, depression, and sleep disturbances. As older adults are more vulnerable and severely affected by the pandemic, they may be at increased psychological risk when seeking to protect themselves from COVID-19. Methods: Our study aims to quantify the association between the stringency of measures and increased feelings of sadness/depression in a sample of 31,819 Europeans and Israelis aged 65 and above. We hypothesize that more stringent measures make it more likely that individuals will report increased feelings of sadness or depression. Conclusions: We found that more stringent measures across countries in Europe and Israel affect the mental health of older individuals. The prevalence of increased feelings of sadness/depression was higher in Southern European countries, where the measures were more stringent. We therefore recommend paying particular attention to the possible effects of pandemic control measures on the mental health of older people.

Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States.

This paper examines the effects of stringency measures (provided by the Oxford Coronavirus Government Response Tracker) and total time spent away from home (provided by the Google COVID-19 Community Mobility Reports) on the COVID-19 outcomes (measured by total COVID-19 cases and total deaths related to the COVID-19) in the United States. The paper focuses on the daily data from March 11, 2020 to August 13, 2021. The ordinary least squares and the machine learning estimators show that stringency measures are negatively related to the COVID-19 outcomes. A higher time spent away from home is positively associated with the COVID-19 outcomes. The paper also discusses the potential economic implications for the United States.