物流中心作业成本通用模型研究

为了实现流通企业物流中心的精细化成本管理,在分析作业成本法原理的基础上,结合流通企业物流中心的业务流程及其生产成本构成特点,本文构建了物流中心作业成本通用模型,并对通用模型中资源池的建立、作业池模型设计、确认成本核算对象、成本动因模型设计四大主要环节的成本核算过程和方法进行了研究和阐述,最后通过实例说明了作业成本法的应用。     

基于ABC分类的卷烟配送中心库存量确定

通过对卷烟出库历史数据的ABC分析,提出了一种基于ABC分类的确定各个品种卷烟库存量的方法,并给出了实际的应用案例。使用结果表明,利用该种方法,在降低各个卷烟品种库存量、减少库存空间占用、加快卷烟出入库速度等方面取得了较好的运用效果。     

Crime and benefit sanctions

In this paper we look at the relationship between crime and economic incentives in a different way to other work in the economics of crime field. We look at empirical models where a toughening of the unemployment benefit regime can be used to study how people on the margins of crime may react to changes in economic incentives. We present three sets of complementary evidence, all of which show that toughening the benefit regime can have an unintended consequence, namely increases in crime. The first approach presents quasi-experimental evidence, looking at crime rates in areas of England and Wales before and after the introduction of a new, tougher unemployment benefit programme—the Jobseekers Allowance (JSA)—in October 1996. The second approach considers qualitative evidence on individuals affected by the change in the benefit regime. The third relates changes in area crime rates to post-JSA sanctions. Each of these approaches uncovers evidence of higher crime occurring as a consequence of the benefit reform.

基于专利技术关联度的中间人角色划分方法研究

基于专利数据构建的发明人合作网络有助于分析发明人中间人角色，揭示发明人创新合作规律，对于企业研发人才管理和知识管理具有重要指导意义。群体划分是中间人角色划分的一个关键步骤，以往文献大多基于网络结构或者给定网络边界进行群体划分，忽视了专利发明人的知识交流环境和特征。鉴于此，基于专利技术关联度，利用专利著录项中的IPC分类号信息判断企业发明人所属技术知识领域，再以技术知识领域为边界设计一套算法，将发明人归入互斥子群，最终对中间人角色进行高效划分，寒武纪公司数据验证了中间人角色划分算法具有较高的准确性和有效性。     

Modelling bank customer behaviour using feature engineering and classification techniques

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper’s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers’ activity.     

Diagnosing housing fever with an econometric thermometer

Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the U.S. housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative “thermometer” for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long-term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy-to-implement reduced-form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the endogenous instrumental variable based method (IVX) to estimate the reduced-form model to reduce the finite sample bias which arises from highly persistent regressors and endogeneity. The recursive evolving test proposed by Phillips, Shi, and Yu (PSY) is applied to the estimated nonfundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY-IVX. An empirical application to the eight Australian capital city housing markets over the period 1999–2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals.     

A multiple criteria sorting method for supporting the maintenance management of medical ventilators: The case of Hospital da Luz Lisboa

Medical equipment is characterized by a constant flow of innovations, which is transforming the delivery of healthcare. This creates the need for healthcare organizations to incorporate methodologies to support the maintenance management of these equipment. Within this context, Luz Saúde, the holding company of one of the largest healthcare groups in Portugal, intends to complement the medical equipment maintenance management program of the biggest hospital in its network, Hospital da Luz Lisboa. To this aim, we develop a decision aiding tool for assessing the maintenance condition of medical ventilators and recommend a plan of action. For that, we apply a multicriteria decision aiding methodology, utilizing the Electre Tri-nC method. In interaction with the decision makers, we construct a model using twelve criteria for assigning ventilators to one of the five ordered categories representing the current condition (excellent, very good, good, adequate, and poor). Most of the medical ventilators in the analysis were considered in adequate or good maintenance conditions, which was consistent with the decision makers’ expectations. A detailed analysis of the results evidenced the robustness of the model. This study constitutes the first step toward the use of multicriteria methodologies for supporting decision processes in Hospital da Luz Lisboa.     

Predictors of revenue shifting and expense shifting: Evidence from an emerging economy

Prior literature established that managers engage in Revenue Shifting (RS) and Expense Shifting (ES) with an intent to report favourable operating performance; our paper extends such research in a new direction by investigating both forms based on the need, ease, and advantage of each form of shifting strategy. The study identifies firm-specific factors that incentivize firms to prefer RS over ES and vice-versa. We undertake a longitudinal study (2001–2019) using a sample size of 39,634 firm-years, enlisted in the Bombay Stock Exchange (BSE). Our results show that peer-performance, size, financial leverage, growth opportunities, accounting flexibility, and age of the firm are important determinants of RS and ES. Specifically, our results exhibit that large, levered, old, and high-growth firms are engaged in RS, whereas small, young, firms with lesser accounting flexibility, and firms operating below peer-performance are involved in ES. These results are robust to controlling for accruals earnings management, real earnings management, endogeneity, self-selection bias, and alternative measures of RS and ES. Our findings are helpful to auditors and investors in improving awareness of forms of classification shifting.