教育驱动的县域城镇化与规划政策反思

县域是完成农民就近城镇化、实现城乡要素跨界配置、促进一二三产融合发展最为基础的空间单元.在一些户籍人口规模大的县域,县城公共服务设施的提标扩面是保障城镇化质量的关键,而教育设施至关重要.通过剖取皖北太和县重点研究,发现该类县城呈现出非常明显的基础教育驱动人口集聚的城镇化景观,现象背后折射出农业转移人口以家庭为单位用时空成本换代际流动的诉求,即该类城镇化的动力机制.农村学生家庭在乡城迁移过程中发生了从"半耕半工"向"半工伴读"的分工模式转变,家庭成员因就近陪读和远距离务工相结合而呈现横跨乡村—本地县城—异地大城市的时空离散.以教育为媒介,上一代在付出巨大时空成本的同时,下一代进行着"城市性"的生活方式、思维模式的社会习得,积累融入社会阶层体系的文化资本,乃至以考学作为通道,实现基于代际之上的城镇化.研究对于具体规划实践中,认识县域城镇化的特征趋势、扩大县城公共服务设施布局中教育资源的供给,提供了更为坚实的实证基础.     

资本外逃的经济效应与应对政策选择研究

资本外逃是一个复杂的经济问题,是由多种原因造成的,并会给一国经济发展带来不可估量的风险和损失。基于此,论文梳理了资本外逃的研究现状,提出了资本外逃所产生的经济效应,在此基础上,提出了资本外逃的政策选择,为一国在治理资本外逃时提供了政策选择和借鉴。     

基于Python平台通过单因子指标法进行地下水质量评价

水质评价关系到水质质量,常见的评价方法有层次分析法、综合评价法等,这些方法评价过程非常复杂,单因子评价方法相比具有操作容易.实际中单因子进行评价时存在概念理解难、计算容易出错等问题.为解决单因子计算问题,本文提出采用Python编程语言进行单因子评价.本文详细分析了Python进行单因子评价流程,从数据采集、数据清洗、数据处理、系统实现进行分析.应用结果表明基于Python编程的单因子评价方法具有评价效率高、准确度高优点.     

新能源材料物理化学课程教学改革探究

新能源材料物理化学课程内容抽象、概念多、涉及面广、公式应用严格,与生活和科学前沿联系紧密。为提高课程教学质量,教师引入案例教学抓住课程核心;联系材料发展历史,增加课堂教学的趣味性;结合科学发展前沿,使教学内容能够与时俱进;注重概念和公式的融会贯通,增加材料调研大作业;线上线下教学模式相结合,采用多样化的教学方法及教学手段;通过形成性评价考核,加强教学过程监管。     

政府购买服务中高校参与及提升服务能力路径研究

高校是政府购买服务的承接方,但其对政府购买服务的响应还处在起步探索阶段。文章首先厘清政府购买服务中高校的角色定位,接着从外在驱动和内在诉求两个角度分析了高校参与政府购买服务的可行性,并在此基础上阐述高校参与政府采购服务的实施路径以及提升服务能力的路径。文章认为,高校应该把握好参与政府服务项目这个良好契机,以积极的态度参与政府购买服务,拓宽自身的发展路径,促成政府和高校实现双赢。     

How is Benevolent Leadership Linked to Employee Creativity? The Mediating Role of Leader–Member Exchange and the Moderating Role of Power Distance Orientation

Previous research has shown that virtuous leader behavior in the form of benevolent leadership has considerable impact on employee creativity. However, little is known as to how and under what conditions these constructs are linked. In the current research, we proposed and tested a moderated mediation model positing leader–member exchange (LMX) as a mediator, and employee power-distance orientation as a moderator of this relationship. Two studies were conducted to test our hypothesized model. In Study 1, repeated measured data collected from 284 Chinese employees in an information technology company demonstrated that benevolent leadership had a lagged effect on LMX. In Study 2, analyses of multisource and lagged data from 391 Chinese employees in 42 research and development teams, and their direct supervisors indicated that benevolent leadership was positively related to supervisor-rated employee creativity via LMX. In addition, the relationship between benevolent leadership and LMX was stronger for employees high in power-distance orientation. Theoretical implications of benevolent leadership’s research and practical contributions concerning promoting creativity in organizations where benevolent leaders prevail are also discussed.     

跨越竞争丛林与范式重构：全球治理改革进程中的疫苗产业创新治理

疫苗作为新冠疫情防控的最关键手段之一，已在各国抗疫中发挥积极影响。疫苗研发、生产、注射意愿以及全球分配的公平性与可及性等一系列问题日益受到世界关注。分析全球治理改革中，疫苗作为一种全球公共产品的创新治理问题，讨论疫苗产业发展特征与创新规律演化，剖析西方国家疫苗产业创新治理困境以及“竞争丛林”逻辑，阐释中国行动方案及对全球治理改革的贡献。最终提出疫苗产业创新治理机制对推动全球治理改革的政策含义。     

Board directors with foreign experience and stock price crash risk: Evidence from China

This study examines the impact of board directors with foreign experience (BDFEs) on stock price crash risk. We find that BDFEs help reduce crash risk. This association is robust to a series of robustness checks, including a firm fixed effects model, controlling for possibly omitted variables, and instrumental variable estimations. Moreover, we find that the negative association between BDFEs and crash risk is more pronounced for firms with more agency problems, weaker corporate governance, and less overall transparency. Our findings suggest that the characteristics of board directors matter in determining stock price crash risk.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.