川西北高原藏区农村贫困化分异机制探测

[目的]客观揭示农村贫困化地域分异机制,为科学推进川西北高原藏区精准扶贫创新途径、综合战略和认真贯彻落实中央治藏方略提供参考依据。[方法]应用地理探测器模型、GIS空间分析与地统计方法等方法,探测川西北高原藏区贫困村单位面积GDP分异的主导因素,揭示农村贫困化分异机制,提出不同贫困化地域类型的扶贫政策措施。[结果]影响川西北高原藏区农村贫困化分异的主导因素包括到主要交通道路距离、归一化植被指数(NDVI)、土地利用、年均温度和海拔高程等,各因素对贫困村经济发展分异的解释力分别为80. 76%、12. 82%、8. 82%、5. 45%和3. 96%;贫困村经济发展贫困化的分异机制存在明显差异,可归纳为交通区位约束型、自然环境约束型和经济区位约束型等三大类型;农村贫困化驱动机制下的贫困村精准扶贫政策措施亟需因地制宜、科学推进、讲求实效,有序推进精准扶贫战略。[结论]精准扶贫应分类指导、重点突破,尤其加强贫困村交通、水利、公共基础设施建设和特色农牧业产业培植,注重多种扶贫模式的综合集成。     

农机社会化服务采纳、禀赋差异与农村经济相对贫困缓解

进入以缓解相对贫困为重点的后扶贫时代，农村经济相对贫困问题仍不容忽视。农机社会化服务作为小农户与现代农业有机衔接的桥梁，对于推进乡村振兴战略和脱贫攻坚有序衔接具有重要作用。文章讨论的核心议题是农机社会化服务采纳如何缓解农村经济相对贫困，并尝试建立了“农机社会化服务采纳——禀赋差异缩小——农村经济相对贫困缓解”的理论分析框架。为此，文章首先对农机社会化服务采纳影响农村经济相对贫困的作用机制进行了理论分析，然后利用2750份河南省农户问卷调查数据，运用Logit模型，实证检验农机社会化服务采纳对农村经济相对贫困的影响，并通过替代核心自变量和变更样本的方法进行稳健性检验，运用PSM模型和IVProbit模型以及ERM模型进行内生性讨论，最后通过中介效应模型对其作用机制进行验证。结果表明，农机社会化服务采纳对农村经济相对贫困具有显著负向影响，缓解了农村经济相对贫困。考虑内生性后，结论仍具有稳健性。进一步的机制验证发现，农机社会化服务采纳通过缩小劳动力禀赋差异和技术禀赋差异，缓解农村经济相对贫困，劳动力禀赋差异缩小和技术禀赋差异缩小的中介效应占比分别为21.22%和7.36%。可见，应将完善农机社会化服务供需体系纳入农户相对贫困治理机制考虑范畴，引导农户从传统家庭化生产转向现代社会化生产，扩大农机社会化服务采纳范围；同时完善就业服务等相关配套体系以吸纳释放的弱劳动力，促进农户收入增长，缓解农村经济相对贫困。     

基于均方差法的郑州市土地综合承载力评价

［目的］开展土地综合承载力评价是科学编制国土空间发展战略的基础。通过构建包含水土资源承载力、经济承载力和生态承载力3个维度土地综合承载力评价指标体系，定量刻画郑州市城市土地综合承载力的特征与轨迹。［方法］运用均方差法，选取2010—2016年郑州市城市土地综合承载力评价指标，分析其土地综合承载力特征。［结果］（1）总体上，郑州市城市土地综合承载力不断增强，水土资源承载力呈现先升—降—升的动态演变特征； 经济承载力持续增长，这源于郑州市土地经济效益的大幅提升； 生态承载力呈现波动性增强的演变特征。（2）郑州市城市土地综合承载力经历了较低—中等—较高发展历程，但土地综合承载力处于低层次的较高阶段，仍有较大提升空间。（3）为提高郑州市城市土地综合承载力，应加快“多规合一”规划的编制，科学划定城市增长边界和“三生”空间，转变城市发展理念，优化发展发展路径，提升城市土地综合承载力水平。［结论］郑州市城市土地综合承载力仍有较大提升空间，应加快建立土地综合承载力扩容的有效机制。     

高管校友关系与业绩预测披露行为

本文考察高管对个人社会关系的依赖是否对上市公司的信息环境产生冲击;具体的,本文系统论证了高管校友关系和上市公司信息披露的重要载体—管理层业绩预测之间的关系.结果显示,当公司与其供应商的高管之间存在校友关系或校友关系更广泛时,公司发布管理层业绩预测的概率和频数均显著下降,即高管校友一定程度上取代了业绩预测,成为了供应链上的信息流通渠道.进一步分析显示,当公司的私有信息成本较高或经营环境更不确定时,校友关系对业绩预测的替代作用更强;而当供应商议价能力较强时,这种替代作用显著下降.此外,本文发现高管校友关系也显著降低了业绩预测发布的自愿性和及时性.最后,本文发现校友关系引起的业绩预测披露减少,削弱了外部信息使用者获取公司层面信息的程度,表现为股价同步性和分析师预测分歧度加大.本文结果表明,高管对个人社会关系的依赖降低了上市公司的信息环境质量,可能使中小投资者处于更弱势的地位,一定程度上损害了市场公平性.本文将个人社会关系的经济影响拓展到信息环境领域,为社会关系如何影响公开信息披露行为提供了新的经验证据.     

Go beyond tokens: Number and type both matter

Drawing upon token theory and critical mass theory, this study examines whether all female directors are equally considered as tokens. We argue that inside female directors are less likely than outside female directors to be considered as tokens because they are more powerful and influential. Using a 10-year sample of Chinese publicly listed firms on the Shanghai or Shenzhen Stock Exchanges from 2008 to 2017, we find that even one inside female director can make a significant contribution to corporate social responsibility (CSR), while outside female directors do not have a positive effect on CSR until their number reaches the critical mass (i.e., three). The results suggest that the predictions of token theory and critical mass theory vary across female director types. These findings contribute to the current literature by integrating female director typology with their numerical representation to explain their token status.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Impulse response function analysis of the impacts of land value-added tax policy on government performance

In this study, an epsilon-based network data envelopment analysis is employed to construct assessment mechanisms for government performance. Moreover, performance indicators of two dimensions of tax collection efficiency and financial effectiveness are measured. We propose a vector autoregression model in which all economic variables are regarded as dependent variables to address the disadvantages of traditional regression model. The conclusions are as follows: (a) measures of tax collection efficiency deteriorated, whereas those of financial effectiveness improved. (b) In an impulse response analysis of the model, an increase in government-published land values produced significantly increased tax collection efficiency.     

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.