郑斌六招制胜创伟业

郑斌同志具有良好的政治素养和强烈的事业心、责任感,思想解放,思维敏捷,思路清晰,具有很强的大局意识和解决问题的能力。他所带领的企业曾先后荣获“全国五一劳动奖状”、“全国优秀施工企业”、“全国守信用,重合同企业”、“全国精神文明建设先进单位”、“铁道部火车头奖杯”、“湖北省优秀企业金鹤奖”,     

新冠肺炎疫情后国内医院建设分析

医疗健康产业是未来发展的朝阳产业,也是健康中国国家战略的重要组成部分,全国各地区医疗设施建设发展不均衡,新冠肺炎疫情又给国内医院建设带来了新的影响和变化.本文调查研究了国内各省在疫情发生后复工复产期内完成招标的医院建设项目的相关数据,并以此作为依据对全国医疗产业发展的区域特征和新冠疫情后医院建设的变化趋势进行分析.     

No,I Do Belong: How Asian American and Asian Canadian Professionals Defy and Counter Workplace Racial Violence during COVID-19

We explore the different types of racial violence encountered by Asian American and Asian Canadians (whom we refer to as Asians) in the workplace during COVID-19 and how they respond. Using a grounded theory approach, we found that during the COVID-19 pandemic, Asians experienced different types of workplace racial violence, most of which manifested as microaggressions, including a revival of the yellow peril trope, physical manifestations of bordering behaviour, and identity denial. In some cases, manifestations of physical violence also emerged. The data revealed that Asians demonstrated various types of agentic responses to challenge and counter unwanted and incorrect identities conveyed by the racial microaggressions. We enhance theory by shedding light on the experiences of Asians whose voice has largely been ignored in the organizational literature. Our study draws together and contributes to the theory on racial violence and racialized identity by highlighting the different types of racial violence faced by Asians and exploring the challenges they encounter in the face of racial microaggressions. Finally, we discuss practical implications of our study results and offer insight into how organizations can help support their Asian employees.     

新冠肺炎疫情后国内医院建设分析

医疗健康产业是未来发展的朝阳产业,也是健康中国国家战略的重要组成部分,全国各地区医疗设施建设发展不均衡,新冠肺炎疫情又给国内医院建设带来了新的影响和变化.本文调查研究了国内各省在疫情发生后复工复产期内完成招标的医院建设项目的相关数据,并以此作为依据对全国医疗产业发展的区域特征和新冠疫情后医院建设的变化趋势进行分析.     

Nowcasting unemployment insurance claims in the time of COVID-19

Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias–variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.     

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.     

Testing the predictive accuracy of COVID-19 forecasts

We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1 week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4 week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.     

US state-level economic freedom during the COVID-19 pandemic

The COVID-19 pandemic led to extensive new government regulations and lockdown policies that significantly reduced economic freedom across US states. Many of these new pandemic-related regulatory restrictions on economic freedom varied significantly from state to state but are largely missed by the existing Economic Freedom of North America Report (EFNA). This paper adjusts the Our World in Data COVID-19 Stringency Index into a state-level measure of lockdown regulatory freedom and then merges it into the EFNA index to better measure relative economic freedom across US states in 2020. We find significant differences in the relative ranking of economic freedom between states once we adjust for lockdown regulatory restrictions.     

浅谈新型冠状病毒肺炎(COVID-19)对经济的影响

中国新型冠状病毒肺炎事件虽然还没有结束,但是它对中国和世界来说都属于“黑天鹅”事件,对国际和国内的经济状况和发展都有短期和长期影响。     

Debt and currency value during COVID-19 in the Global South

The aim of this article is to examine debt and currency vulnerability during economic turbulence in the Global South. A panel data analysis is performed on a panel of 25 economies consisting of nine developed and 16 developing countries with a focus on public and private debt. The empirical findings reveal that only public debt build-up has an adverse effect on currency value. There is no evidence of a significant impact of recession periods on currency value for both private and public debt estimations. I also found that private debt build-up can be more harmful than public debt in developing countries. In addition, both public and private debt have increased as percentages of GDP during COVID-19.     

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.     

The impact of the COVID-19 pandemic on business expectations

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms report significant or severe disruptions to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruptions, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively impacted by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms’ one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms’ near-term inflation expectations, their longer-run inflation expectations have remained relatively stable.     

A description of the COVID-19 outbreak role in financial risk forecasting

This study aims to describe the risk of the system composed on the market indexes of the countries that were more affected by COVID-19. Our sample encompasses the thirty-five countries with more cases and/or deaths caused by COVID-19 until November 2020. As a second contribution, we describe the risk of each market index individually. As a general pattern, we note that losses and individual and systemic risks peaked in March 2020. We verify that countries that were epicenters of the COVID-19 pandemic experienced critical levels of risk, which is partially explained by more stringent confinement measures since these are the ones whose labor markets will suffer more in the medium and long run. We perceived a market recovery, arguably due to the low-interest rates and expansive actions taken by central banks. Nonetheless, we also observed that the systemic risk returned to pre-pandemic levels at the end of 2020.     

工作场所出勤主义生产率减损测量研究的述评与展望

出勤主义是指工作场所员工带病工作的行为.这种行为不仅会影响员工个人的身心健康,也会造成企业生产率的减损.测量出勤主义的生产率减损后果是出勤主义领域研究的重点和难点.通过对20世纪90年代至今的国外文献进行回顾与梳理,在对出勤主义测量工具开发动机和整体情况进行总结的基础上,重点分析现有工具在测量出勤主义的生产率减损后果时采用的不同方法,减损结果可否量化为具体的生产率减损数值,相关生产率减损的货币转化等三个问题.最后,针对出勤主义生产率减损测量研究的局限性,对未来研究进行了展望.     

Managing the retail operations in the COVID-19 pandemic: Evidence from Morocco

Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest-based prediction model with K-cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision-making model for BSE. From the analysis, the current retail performance index has been computed as “Average” level and the graph-theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning-based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events.