税收优惠影响企业创新产出的多元机制研究——来自中国软件与集成电路产业的证据

创新政策一直是激励企业创新的重要手段，其中税收优惠政策是一种具有市场导向性的激励政策，对企业创新具有良好的促进作用，但是政策对企业产生激励作用的原因尚不清晰。选取对我国技术突破有重要作用的软件与集成电路企业，采用2006-2018年中国A股上市公司数据，聚焦2011年国家对软件与集成电路行业的两个税收优惠政策，运用双重差分法对企业创新产出影响进行实证研究。结果发现：税收优惠政策对发明专利申请有显著促进作用，而对非发明专利申请无显著影响。融资约束、企业避税行为等是税收激励政策发挥作用的主要渠道。另外，税收优惠政策对不同规模、产权和区域企业有不同影响，税收优惠政策对大型企业、国有企业以及中西部地区企业创新产出促进作用更大。从税收优惠政策长期影响看，该政策对专利申请的影响存在时滞性，而发明专利申请在政策发生当年就显著增加。研究结论可为明晰税收优惠政策、融资约束与企业创新产出间关系提供有益启示。     

重要生态用地级别评价和划定方法研究——以水源涵养为例

文章以水源涵养为例,基于水平衡评价计算方法,选择水资源禀赋类型不同的县域单元开展水源涵养型生态用地功能分级划定研究。总结现有生态保护红线划定技术方法及存在不足,致力于显化不同地区水源涵养生态用地功能绝对值差异,利用评价单元格水源涵养累计贡献值面积弹性拐点,对重要水源涵养用地级别划定方法进行改进。对河北省易县和四川省金川县的实例验证表明,此评价方法可以根据不同地区水源涵养功能特点量身确定重要生态空间规模、面积和比例。     

生计资本对黄土丘陵区贫困人口生计活动的影响及评价*——以甘肃省榆中县为例

［目的］通过深入研究黄土丘陵区贫困人口生计资本对生计活动的影响，为解决农村贫困问题，提升农户生计资本存量、实现区域可持续生计发展提供科学依据。［方法］文章以甘肃省榆中县为例，基于对榆中县农户的调查数据，采用熵值法分析了东北部山区、中部川区和西南部山区贫困人口的生计资本状况和生计活动方式，并对其生计活动进行评价。［结果］从农户的生计资本组合来看，榆中县整体自然资本和人力资本相对物质资本、金融资本、社会资本3类资本较为富裕，尤其在西南部山区和东北部山区3类资本缺乏型农户比例较高。中部川区各乡镇人口的总体生计资本状况较好，总指数最高，而西南部山区次之、东北部山区最低，两者生计资本状况总体匮乏。农户的生计活动主要以种植、养殖和外出打工为主，生计活动多样性程度偏低； 中部川区人口的生计活动多样性指数最高，西南部山区次之、东北部山区最低，且整体受教育程度偏低，加之物质资本、金融资本、社会资本缺乏进一步限制了当地农户的生计多样性。［结论］（1）针对不同生计资本水平和生计活动多样性程度偏低的贫困地区，需要进一步发挥民族文化产业扶贫优势，开启地域产业扶贫新模式，打造地域无公害特色农业产业品牌，提高农户物质资本边际贡献； （2）发展小额信贷金融服务，改善农户金融资本存量，预防村际农户金融资本二元极化； （3）实施免费教育培训技能学习，打造教育培训脱贫示范村，提高人力资本存量，缓解贫困人口生计压力等提高经济发展水平的扶贫政策措施。     

Measuring firm performance: Differentiating between uncontrollable and controllable bad outputs

This paper introduces a new model that differentiates between controllable and uncontrollable bad outputs in the measurement of firm performance; the airline industry is used as the empirical example. The model allows us to measure technical inefficiency, as well as the inefficiency in the production of bad outputs, and the inefficiency in the investments allocated to the reduction of uncontrollable bad outputs. Moreover, we provide a direct measure of the total cost of controllable bad outputs. As the model involves a number of latent (unobservable) variables that cannot be easily integrated out of the likelihood function without producing complicated expressions, we resort to Bayesian techniques. We find notable differences in inefficiency across airlines. More importantly though, we find that the production of bad or undesirable outputs impose significant costs on airlines. However, these cost issues could be reduced if airlines invest more in maintenance and service training.     

盐城市水资源承载状态预警研究

在分析水资源承载状态预警概念的基础上,引入"三类六级"预警思想,采用可变模糊法和层次分析法的思想,构建盐城市水资源承载状态综合预警指标体系,评价盐城市2015年、2020年和2030年的水资源承载状态,并结合各年份承载状态变化趋势,对未来年份进行预警。结果表明:盐城市2015年水资源处于临界超载的状态,但随着"关于做好建立全国水资源承载能力监测预警机制工作的通知"等一系列措施的实施,2020年、2030年承载状态将逐步好转,到2030年将处于可载的状态。     

Financial development,economic growth and convergence clubs

To study the role of financial development in economic growth, we apply an array of convergence tests designed to capture nonlinear transitional dynamics to real outputs per capita. Strong evidence of multiple convergence clubs is observed, implying that the clubs are formed based on the initial level of real output per capita and average growth rate. Our empirical results show that the stage of economic growth of each country plays an important role for the composition of the convergence clubs. Furthermore, financial development emerges to be a significant determinant, albeit plays differently in the economic growth of each convergence club.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.     

Operational excellence in a green supply chain for environmental management: A case study

Nowadays, organizations have started to become more conscious about the environment in their supply chain operations. The greening process has guided supply chain practices into new ways of thinking according to green standards. The assessment of the performance of green supply chain management (GSCM) requires a holistic view for the whole supply chain. In this context, given that becoming green in the operational side of activities is essential, the performance assessment of operational activities also requires a holistic view to be taken. In this paper, an attempt has been made to improve the performance of GSCM by examining and evaluating the green operational excellence of a hot dip galvanizing company. The framework includes several green operational excellence key criteria, namely, quality management, efficiency management, green production/manufacturing, eco‐packaging, and green design. First, the weights of the criteria and the respective measurements were found by fuzzy analytic network process. Then, the overall operational performance score was found by a weighted scoring method. Finally, both managerial and theoretical implications were suggested according to the outcomes and findings of the case study.     

基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题，利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性，提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后，利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能，将NBI稀疏分块的块内自相关矩阵建模为单位矩阵，提出了信息辅助BSBL(Aid BSBL,ABSBL)算法，设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下，提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明，所提方法能够有效抑制DSSS通信中的NBI，在干扰强度相同的条件下，NBI带宽越小、压缩率越大，算法对NBI的抑制性能越好。