Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

The role of socially rich photos in generating favorable donation behavior on charity websites

While online retail sites succeed in reinforcing perceptions of human warmth on their websites thanks to more and more interactive technologies, the charitable sector cannot, for lack of human, technical and financial resources, make use of such technologies. This research paper demonstrates the potential of a simple tool: the socially rich photo. Although the literature has extensively documented how photos impact visitor behavior in online environments, research in the context of donations through charity websites is rare and has provided contradictory results. By focusing more closely on the role of the individual represented in such photos (the recipient of a donation versus the donor), our studies, conducted in France, offer additional insights on the subject as well as managerial advice for organizations.     

税收优惠政策与高技术产业创新效率

研究目标：分析税收优惠政策对创新效率的影响以及创新效率的收敛性，测度税收优惠政策对创新带来的实际贡献。研究方法：构建随机前沿模型测度高技术产业的创新效率，采用σ收敛和β收敛检验方法检验创新效率收敛性，并采用反事实计量方法测度税收优惠政策对创新带来的实际贡献。研究发现：税收优惠政策对研发效率有显著正影响，对市场转化效率的影响为正但不显著，政策实施后效率分布也更集中；研发效率的收敛主要是由东中西地区内部之间差距的缩小造成的，而市场转化效率的收敛是由三大地区内部差距的缩小以及中西部和东部地区的差距缩小共同造成的；税收优惠政策使研发效率大约上升了8%～10%，带来的创新产出占总产出的比重也一直维持在10%以上。研究创新：从效率水平和收敛性两个角度来定量分析税收优惠政策对创新效率的影响。研究价值：为更好地采取措施提高创新效率和减小地区创新效率差距提供经验证据。     

Assessing the global productive efficiency of Chinese banks using the cross-efficiency interval and VIKOR

The banking industry plays an essential role in China's economic growth, and the assessment of bank efficiency has attracted increasing interest in the past two decades. Given that the data envelopment analysis cross-efficiency method can provide a more reliable reference set, a large number of studies targeting performance evaluation and its applications adopt this method. Few studies, however, address the issue of the non-uniqueness of weights in cross-efficiency evaluation. Whereas, this paper proposes a cross-efficiency interval considering all weighting schemes along with a VIKOR aggregate model to gauge the universal productive efficiency of 16 major Chinese commercial banks of China and rank them for the period of 2007–14. Specifically, we use the cross-efficiency interval to generate the global performance of each bank based on internal and external assessment while using VIKOR to compare the solution with the attitude index which ranks all of the banks. The results indicate that the cross-efficiency interval can provide more information than the traditional DEA model, that the banking system efficiency of China has been improved during the study period, especially for SOBs; that JSBs are more efficient compared with SOBs; and the efficiency gap narrowed during the study period.     

Market Access,Labor Mobility,and the Wage Skill Premium: New Evidence from Chinese Cities

Open Economies Review - Labor market reforms in developing economies have causes economic regions to open up, providing greater market access to businesses and leading to urban migration. This...     

New evidence on income and the geographical distribution of imports: The case of audiovisuals

This paper studies how the geographical distribution of imports, in the audiovisual sector, varies with aggregate income across countries to confront existing fears of cultural homogenization with increasing purchasing power. Relying on new predictions derived from an heterogenous firms model (Helpman, Melitz & Rubinstein, 2008), two dimensions of diversity are investigated: the number of geographical origins (extensive margin) and the distribution of audiovisual imports across exporters (intensive margin). The empirical results reveal that per capita income fosters diversity up to point: income first has a positive impact on the extensive margin, yet beyond a certain threshold, the number of import sources declines. Regarding the intensive margin, per capita income reinforces the concentration of import flows across origins benefiting the most efficient partners.     

The land rental of Chinese rural households and its welfare effects

By using the survey data of China’s rural households from 2003 to 2009, we identified the determinants of renting in and out the plots and estimated their impact on household welfare. First, we developed a bivariate dynamic probit model for the behaviors of renting in and out to manage the impact of the intertemporal decision-making that has been somewhat ignored by the literature; next, we developed a panel multiple endogenous treatment effect model for land rental impact to manage the self-selection problem stemming from the unobserved variables. The results show that regardless of renting in or out, both scenarios have statistically significant positive effects on the net income and balance of disposable financial assets, and renting out also has a significant positive effect on consumption expenditure.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

公司社会声望与高管薪酬:公共服务抑或职业声誉

本文基于“隐性—显性”契约激励研究范式,探讨公司社会声望对高管薪酬的影响以及作用机制。本文选取2009—2017年间的非金融A股上市公司为样本,研究发现,拥有较高社会声望的公司,其高管显性薪酬较低。具体而言,公共地位较高的国有企业、具有较高市场声誉的民营上市公司,其高管薪酬平均而言分别比其他上市公司低4.97%和6.30%。进一步地,我们发现公司声望对我国高管显性薪酬契约存在两种作用机制:一方面,公共地位较高的国有企业,可以为高管带来较高的社会声誉和社会认可,满足了“公共服务”类高管的社会声望偏好,从而降低了显性薪酬的支付水平;另一方面,市场声誉较高的民营企业,可以为高管带来较高的职业声誉和未来职业利益,符合“以商为荣”类高管的社会声望偏好,使高管愿意接受较低的显性薪酬。本文的结论为公司声望作为一种有价值的资源,可以对高管显性薪酬形成议价能力提供了重要证据,揭示了公司声望对高管显性契约激励的影响路径;同时,为国有企业高管薪酬契约设计以及激励机制提供了一定启示。     

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。