大数据对法院司法统计的积极功效及边界

最高人民法院于2016年应用人民法院大数据管理和服务平台以后,全国法院司法统计完全实现计算机自动采集。从发展脉络来看法院司法统计正处于智能化转型阶段,仍然存在一些问题。随着大数据及新技术的引入,司法统计“大数据”得以成为现实,包括了数据采集的全面化、统计分析的综合化、统计平台的社会化,进而提出了大数据助力司法统计精准化的具体路径,包括了提高基础数据的量与质、加强对司法统计数据的深度分析和可视化分析、推进基础设施建设。然而,二者并不具有正相关性。因此,推进司法统计大数据化需要注意有两个限度。     

城镇化背景下治安治理社会化的探索与创新——以钱江世纪城派出所“物业联席会”创建为例

治安治理社会化的核心是社会力量的参与。从宋代的民治传统到建国以来的政策与实践探索,从警务社会化、治安管理社会化到治安社会化、社会治安社会化治理等概念的相继提出,构成了治安治理社会化的演进脉络。治安治理社会化的本质是治安秩序维护的多元化,强调多元化共治、过程性理念与互动性思维的有机统一。杭州市公安局萧山区分局以“物业联席会”机制创建为载体,整合社会资源,激发社会组织活力,为治安治理社会化提供了更多可能的路径。借鉴“物业联席会”的创建经验,城镇化背景下的治安治理社会化应坚持党委组织领导,协同推进组织载体创新、联动机制创新与智慧化手段运用相融合。     

基于西门子PLC除尘器粉尘防爆危险源信号检测及预警系统的研究

研发适用于滤袋式除尘器粉尘爆炸危险源信号的实时检测及预警系统。该系统综合应用工业控制、PLC控制、智能测控终端无线传输、云平台数据管理等技术。采用传感器分别监测除尘器运行时的进出风口压差、除尘器运行状态、高压脉冲喷吹压力和箱体温度。当监测信号不在系统预设的合理范围时,PLC输出就地预警信号,监测信号也可通过无线传输模块上传到云平台。运用云平台的数据分析功能,实现除尘器运行状态系统化监测和综合管理。     

Do vertical spillovers differ by investors’ productivity? Theory and evidence from Vietnam

Developing countries are eager to host foreign direct investment to receive positive technology spillovers to their local firms. However, what types of foreign firms are desirable for the host country to achieve spillovers best? We address this question using firm‐level panel data from Vietnam to investigate whether foreign Asian investors in downstream sectors with different productivity affect the productivity of local Vietnamese firms in upstream sectors differently. Using endogenous structural breaks, we divide Asian investors into low‐, middle‐, and high‐productivity groups. The results suggest that the presence of the middle group has the strongest positive spillover effect. The differential spillover effects can be explained by a simple model with vertical linkages and productivity‐enhancing investment by local suppliers. The theoretical mechanism is also empirically confirmed.     

“Opt out” or kept out? The effect of stigma,structure, selection,and sector on the labor force participation of married women in India

Using the 61st and 68th rounds of National Sample Survey data, we investigate the role of stigma, the economy’s structure, potential selection bias, and sectoral differences in explaining the low labor force participation (LFP) of middle and secondary educated women in India relative to low‐ and high‐educated women (the “U‐shape”). Estimating LFP regressions on a sample of prime‐aged married women, we show that, controlling for background characteristics, if a woman’s husband works in a white‐collar job in a region with a high share of blue‐collar jobs, she is less likely to participate in the labor market (stigma). We also find a positive effect of an increase in an index of white‐collar job growth (structure) on married women’s LFP. These effects are present in both rural and urban sectors but are strongest in the rural sector. However, middle and secondary educated women are still found to have substantially lower LFP than low‐ and graduate‐educated women in both sectors. Indeed, over time, the U‐shape persists in the rural sector and deepens in the urban sector. Because unobservables are quite large in the urban sector, we use the method of instrumental variables and find an increasing return at middle levels but stagnation at higher levels.     

去工业化、技术差距与产业转移的技术溢出效应

中西部承接产业转移缺乏显著的正向技术溢出效应，成为2000年后区际技术差距持续扩大以及近年部分地区过早去工业化的关键影响因素之一。利用2001~2017年全国30个省（市）面板数据研究证实，各地区技术水平与工业增加值在地区生产总值中的份额之间存在显著正相关性；中西部在2005~2014年承接了较大规模产业转移，而区际技术差距却持续扩大，技术溢出效应显著为负。在相对偏低的技术水平条件下，中西部地区在十余年的产业承接中主要依赖于廉价的要素成本优势，其自主创新能力、创新资源积累已不足以抵御本地消费升级、要素成本上升与国际需求疲软等负面因素的叠加影响。最终，技术差距、商品及服务净输出赤字的扩大至少加快了部分欠发达地区的去工业化。     

国有企业高管薪酬管制与委托代理问题

由于国企高管的经营业绩难以被准确度量，加上监督约束机制不健全，高管的超额薪酬成为国企代理问题的一种典型表现，于是政府将高管薪酬管制作为应急措施来降低代理成本，缓解国企代理问题。采用双重差分法对2011—2018年A股上市公司的分析表明：第二次限薪令在有效限制国企高管薪酬水平的同时，没有对高管在职消费产生显著影响，有效降低了国企代理成本；但第二次限薪令也降低了薪酬激励效应，导致国企高管努力程度降低，由此造成的利益损失又成为新的代理成本。国企薪酬制度改革面临激励与管制的两难选择，应在国企分类改革的基础上对不同类型国企的高管采取不同的激励机制：对公益类及特殊行业的国企增强高管政治晋升激励，对竞争行业（营利性）国企则应在推向市场的同时放松薪酬管制，从而有效降低代理成本，解决国企代理问题。     

How efficient airways act as role models and in what dimensions? A superefficiency DEA model enhanced by social network analysis

In this empirical study, a five-stage methodology is used to examine the efficiency of 45 worldwide known airline companies from the financial, operation and marketing perspectives. Initially, the superefficient data envelopment model is run with inputs and outputs that are selected based on the literature review. However, because 21 out of 45 airline companies are found to be efficient based on this analysis, a stepwise regression-based mechanism is applied to four reduced models – one for each output variable – for better discrimination. The outputs are, namely, net profit margin (financial output), passengers carried, on-time departure performance (operational outputs), and customer satisfaction (marketing output). In this way, the significant input variables are found for each reduced model. In the third stage, in order to provide even more discrimination, social network-based eigenvector centrality values are used as the weights of the superefficiency scores, and the strengths and weaknesses of efficient airlines for each output are specified in terms of their related significant inputs. The results show that, when net profit margin is taken as an output, Vietnam Airlines has the top weighted superefficiency value and excels in terms of available seat kilometers and liquidity, but it should improve its debt level. Although Norwegian Airlines has the highest efficiency with respect to debt level, it is not the best role model because its eigenvector centrality value is relatively low. However, Norwegian airlines also has the highest weighted superefficiency and acts as a role model in terms of on-time departures with respect to this output. Its main strength is liquidity, and it has no significant weaknesses. On the other hand, in terms of overall satisfaction and passengers carried, Vietnam Airlines and Thai Airways are the leaders, respectively. Vietnam Airlines is the only superefficient company with respect to overall satisfaction, while the basic strengths of Thai Airways in terms of passengers carried are its employee and fleet, and it has no significant weakness. A final aggregation of the results is made by making pairwise comparisons of the relative importance of four outputs for 7 experts selected from different departments of airline companies. According to the results, Net Profit Margin has the highest priority, followed by On-time Departure and Overall Customer Satisfaction, while passengers carried has the lowest importance. Based on these relative priorities, it can be said that Vietnam Airlines can be accepted as the top performing airline company, followed by Norwegian Airlines.     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

Assessing data quality: A managerial call to action

While awareness of data quality has increased in recent years, there have been very few studies on the actual level of data quality within organizations. This article outlines the analysis of 75 data quality assessments collected over the course of 2 years from a wide range of organizations, data sets, and business processes. The results reveal that data is in far worse shape than most managers realize. On average, 47% of recently created data records have at least one critical error. High-quality data is the exception, with only 3% of the DQ scores rated acceptable (≥97%). Indeed, the results suggest an unhealthy organizational tolerance of bad data and underscore the magnitude of improvement organizations need to make in order to be truly effective in the knowledge economy. By providing a unique insight and benchmark for data quality practitioners, this article serves as a call-to-action for all organizations—regardless of size and type—to determine their level of data quality. Finally, we set out a typology that presents a categorical scheme to promote preemptive actions against the most frequent types of data error.