The effect of compulsory face mask policies on community mobility in Germany*

There is an ongoing debate about face masks being made compulsory in public spaces to contain COVID-19. A key concern is that such policies could undermine efforts to maintain social distancing and reduce mobility. We provide first evidence on the impact of compulsory face mask policies on community mobility. We exploit the staggered implementation of policies by German states during the first wave of the pandemic and measure mobility using geo-located smartphone data. We find that compulsory face mask policies led to a short-term reduction in community mobility, with no significant medium-term effects. We can rule out even small increases in mobility.     

Revisiting the safe haven role of Gold across time and frequencies during the COVID-19 pandemic

This research empirically evaluates the potential diversification benefits of Gold during the COVID-19 pandemic period, when including it in equity-based asset allocation strategies. This study proposes minimum VaR portfolios, with monthly rebalance and different wavelet scales (short-run, mid-run and long-run), doing both an in-sample and out-of-sample analysis. We find much more unstable weights as the frequency of the decomposition becomes lower, and strong evidence of the outperformance of the mid-run decompositions over the rest of active management strategies and the passive management of buy and hold the variety of single equity indices. Thus, we may shed some light on the role of Gold as a safe haven when properly filtering aggregated data.     

Nexus between drivers of COVID-19 and country risks

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.     

The role of trust and privacy concerns in using social media for e-retail services: The moderating role of COVID-19

The COVID-19 pandemic has disrupted the customers habits of purchasing as well as shopping behaviours. This study seeks to develop an integrated model of the critical role of trust and privacy concerns in influencing consumers purchase behaviour through social media. It also explored the moderating role of COVID-19 on these relationships. Quantitative data were collected using survey strategy through questionnaires to address different levels of the study. Our proposed model was tested with 1,200 consumers, 600 prior to COVID-19 and 600 during COVID-19. Partial Least Squares Structural Equation Modelling was conducted to assess the hypotheses. The findings revealed that purchase intention depends on trust and privacy concerns. Information quality, security concerns, ease of use, privacy/security assurance seal, and disposition to third party certification are the main drivers of trust and privacy concerns. Furthermore, our proposed model during COVID-19 period has higher explanator power (R2 = 0.741) than before COVID-19 period (R2 = 0.603 and consumers buying behaviour has been increased during COVID-19. The results offer important implications for retailers and are likely to stimulate further research in the area of purchase behaviour through social media.     

Fund immunity to the COVID-19 pandemic: Evidence from Chinese equity funds

This paper discusses the connection between public equity fund characteristics and performance reactions to COVID-19 using data over 1300 equity funds across 105 Chinese fund companies. Empirical evidences from over 20 fund characteristics show that the liquidity, diversification and pre-2020 Sharpe ratio, fund management abilities, agency costs can determine the fund immunity to COVID-19. Based on these characteristics mentioned, our empirical results can explain why COVID-19-induced drop in fund performance is milder among open-end funds, active funds, ETFs, and growth funds, and also can explain why funds controlled by private companies or by sino-foreign joint ventures or by companies with more independent directors of financial experiences perform better in the pandemic. Our work also provides some valuable suggestions for investors and regulators confronting an exogenous shock.     

The transition of the global financial markets' connectedness during the COVID-19 pandemic

This study contributes to the literature on financial research under the presence of the COVID-19 pandemic. Fresh evidence emerges from using two novel approaches, namely network analysis and wavelet coherence, to examine the connectedness and comovement of financial markets consisting of stock, commodity, gold, real estate investment trust, US exchange, oil, and Cryptocurrency before and during the COVID-19 onset. Moreover, unlike the previous studies, we seek to fill a gap in the literature regarding the ex-post detection of COVID-19 crises and propose the Markov-switching autoregressive model to detect structural breaks in financial market returns. The first result shows that most financial markets entered the downtrend after January 30, 2020, coinciding with the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern. Thus, it is reasonable to use this date as the break date due to COVID-19. The empirical result from network analysis indicates a similar connectedness, or the network structure, in other words, among global financial markets in both the pre-and during COVID-19 pandemic periods. Moreover, we find evidence of market differences as the MSCI stock market plays a central role while Cryptocurrency presents a weak role in the global financial markets. The findings from the wavelet coherence analysis are quite mixed and illustrate that the comovement of the financial markets varies over time across different frequencies. We also find the main and most significant period of coherence and comovement among financial markets to be between December 2019 and August 2020 at the low-frequency scale (>32 days) (middle and long terms). Among all market pairs, the oil and commodity market pair has the strongest comovement in both pre-and during the COVID-19 pandemic phases at all investment horizons.     

Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.     

Pandemic and employment: Evidence from COVID-19 in South Korea

Using two complementary approaches, this study examines the deterioration of the Korean labor market during the first 10 months of the global COVID-19 pandemic. Applying the synthetic control method, we first find that the COVID-19 outbreak has eliminated 1.1 million jobs (4.2% of nonfarm employment) nationwide in April 2020. However, a difference-in-differences approach shows that local variation in COVID-19 intensity, which captures the “regional” effect of the pandemic, explains only 9% of the national shock. The portion of the regional effect remains low until December. This is mainly because the nationwide fear and policies such as social distancing measures also have a “common” effect on local economies. Our findings suggest that the COVID-19 shock may last long in the labor market due to this common effect unless the risk of infection is completely eliminated.     

我国敬业度研究综述——基于CSSCI期刊的可视化分析

随着工作加班常态化,敬业度问题受到国内学者的广泛关注且研究成果丰富。文章以中文社会科学索引(CSSCI)为来源,利用CiteSpace绘制文献共被引、关键词共现、关键词聚类等图谱,对敬业度进行可视化分析。研究结果表明,国内关于敬业度的研究处于高速发展时期,但与国外研究相比仍有一定差距;另外,学者们主要围绕如何提升敬业度展开研究,发现通过营造良好的组织环境、采取与员工个人特征相适配的领导风格等可以提升敬业度。今后应进一步拓宽研究领域和研究视角,运用新的理论框架研究不同因素对敬业度的差异化影响,以拓展对敬业度研究的深度和广度。     

新冠疫情管控期间西安市人口分布演变及影响因素探测——基于多源时空大数据视角

新冠疫情的"超常规"管控举措影响了城市人口分布,研究其演变规律有助于疫情管控期间城市人口活动规律认知、聚集趋势判断,以及指导精准化管控分区划分和场所差异化管控。基于百度热力和POI数据,采用人口密度指数、ESDA及地理探测器,分析了西安市在2020年2-4月疫情管控期间的人口分布变化及其影响因素。结果表明:(1)西安市人口密度值及人口密度波动幅度由一环向郊外递减,且人口数量及人口流动性逐渐恢复。与非疫情阶段相比,居民的日常活动周期未发生较大变化,但受复工后企业错峰午休以及居民减少外出影响,活动特征在局部表现不同。(2)西安市高、较高密度区逐渐增加,较低、低密度区持续减少,而随着疫情管控放开及复工复产推进,城市人口分布逐渐由"中心—外围"转变为"中心—外围、开发区延伸"的空间结构。同时,西安市人口分布呈现明显的高、低值聚集以及内热外冷圈层式特征,且随着人口活力的恢复,这种特征越来越显著。在此基础上,进一步总结出防控导向型、防控—就业需求导向型、就业—消费需求导向型、购物—休闲需求导向型四种人口活动模式的特征。(3)西安市7类设施对人口分布的影响强度为:住宅小区>餐饮设施>生活服务>医疗机构>购物服务>办公场所>公园广场,且住宅小区、餐饮设施、生活服务、医疗机构的影响强度逐渐减弱,购物服务、办公场所、公园广场的影响强度逐渐增强,指出疫情管控影响下城市配套设施与人口分布的耦合关系变化是影响西安市人口分布由相对分散变为更加聚集的重要原因。