美英日三国公共财政法律体系及其特点

市场经济发达国家公共财政框架的一个突出特点就是它的法制基础十分完善。尽管各个国家的政治、社会、经济、文化、历史等方面存在着差异,但其共同点是整个公共财政管理框架建立在坚实的法律基础之上,政府财政收支活动的每一个环节都做到了有法可依。了解美国、英国和日本等典型国家财政法制建设的经验,对于研究构筑我国公共财政法律体系框架的思路,无疑是十分有益的。     

财政幻觉假说的实证检验

财政幻觉假说是公共选择理论下解释政府规模增长的理论之一。依据财政幻觉假说的经验方程，将“财政幻觉”分解为“赤字幻觉”与“预期幻觉”，以中国1978～2004年的年度数据，采用邹氏转折点检验、协整分析和误差修正模型，对“财政幻觉”与政府规模增长的关系进行实证检验。结果表明，“财政幻觉”是中国政府规模增长的原因之一，其中“赤字幻觉”的存在未得到显著证实，但“预期幻觉”对政府规模有正的显著影响。     

Poverty and social trends in Russia

The article describes the socioeconomic situation of the Russian population and its dynamics in 1992. The analysis is based on series of regional sociological surveys led by the All-Russian Living Standards Research Center and on official Russian statistical data. The basic unit of analysis is the family, and hence family typology approbated both in research and in practical social work is introduced. Besides describing and discussing the growing poverty tendencies in contemporary Russia, the article also contains methodological recommendations for social policy bodies and social workers on the strategy of work in social maintenance and social support of the least secure population strata.     

浅谈油田企业全面预算管理的若干问题与对策

全面预算管理作为企业战略实施和管理控制的主要手段,已在油田企业普遍实施,但由于各个企业理解的片面性以及处理实务的经验不足等原因,仍对全面预算管理不同程度地存在着一些误区和问题,需要加以研究和解决,以确保全面预算管理能充分发挥其应有的职能和功效。     

非对称情况下的多物品拍卖

本文讨论当投标人之间存在不同的预算约束时两物品的序贯增价拍卖,对于物品之间不同的关系(互补、替代或者不相干的关系),物品价值大小的不同及与预算大小之间的关系,在一个简单的完全信息模型下,我们分不同情况讨论投标人的均衡出价策略,并发现对卖方来说,先拍卖价值高的物品是弱占优的策略。     

因子模型在风险预算分析中的应用

风险预算在国外正赢得学术界、养老基金和资产管理领域等许多方面的广泛关注,并认为是组合管理的一种创新。国内投资者日益认识到投资组合管理与风险控制的重要意义,并近乎成为他们的第一要务。本文利用因子模型,通过风险预算这一投资管理和风险控制技术与方法,讨论投资组合因子风险分解并进行实证分析。     

企业财务预算管理中存在的问题及解决对策

财务预算管理在企业日常经营决策和未来发展中处于核心地位。实际工作中,财务预算管理却存在概念不清、领导重视不够,预算编制缺乏战略指导,预算执行过程缺乏监督控制、执行结果缺乏考核与激励,人员素质低等问题。针对这些问题,企业应采取的对策是:领导重视并亲自抓财务预算管理工作,树立以市场为导向、以企业发展战略和规划为指导的财务预算管理理念,设置相应财务预算管理工作组织,建立职责分工明确的企业财务预算管理机制,提高财务预算人员的综合素质,建立和完善财务信息系统。     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.