控制权私利下的企业资本配置决策研究

基于企业内部资本配置的理论阐释,以固定资产和长期股权的组合投资为研究对象,构建出有无大股东控制下的企业资本配置模型,理论分析控制权私有收益对企业资本配置决策的影响.研究结果表明:大股东的控制权私利行为是导致企业偏离最优资本配置决策的一个重要原因,偏离程度随现金流权与控制权分离度的增大呈非线性变化.该变化趋势主要由固定资产投资收益率和长期股权投资收益率的大小决定.     

技术不确定条件下的技术创新投资决策分析

技术不确定性是技术创新投资的主要特点。在技术创新投资模型的基础上引入技术不确定性,从而得到双头垄断企业在技术不确定条件下的最优投资时点。同时,在此基础上分析了技术不确定性及技术不确定的相关度对投资时点的影响。     

Security Investment in Contagious Networks

Security of the systems is normally interdependent in such a way that security risks of one part affect other parts and threats spread through the vulnerable links in the network. So, the risks of the systems can be mitigated through investments in the security of interconnecting links. This article takes an innovative look at the problem of security investment of nodes on their vulnerable links in a given contagious network as a game‐theoretic model that can be applied to a variety of applications including information systems. In the proposed game model, each node computes its corresponding risk based on the value of its assets, vulnerabilities, and threats to determine the optimum level of security investments on its external links respecting its limited budget. Furthermore, direct and indirect nonlinear influences of a node's security investment on the risks of other nodes are considered. The existence and uniqueness of the game's Nash equilibrium in the proposed game are also proved. Further analysis of the model in a practical case revealed that taking advantage of the investment effects of other players, perfectly rational players (i.e., those who use the utility function of the proposed game model) make more cost‐effective decisions than selfish nonrational or semirational players.     

资本结构与员工劳动生产率

债务作为重要的公司治理手段,可以降低企业、管理层与员工之间的委托代理成本,提高员工劳动生产率.同时,债务水平上升将提高企业破产风险,对企业和员工的人力资本投入与员工劳动生产率可能产生不利影响.以我国1999年一2014年工业企业上市公司为样本,本文不同佑计方法的实证结果均表明企业的债务水平越高,员工劳动生产率越低.在其他因素不变的情况下,企业的财务困境加剧了债务对员工劳动生产率的不利影响;债务对员工劳动生产率的负作用在人力资本依赖度高的公司以及在外部就业环境好的地区更加显著.对于债务与员工劳动生产率作用的中介效应分析与分位数回归结果也说明债务上升可能导致企业人力资本投资下降,对员工劳动生产率产生不利影响.研究为我国“去杠杆”经济政策提供了微观企业层面的经验证据.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

Risk–risk governance in a low-carbon future: Exploring institutional,technological, and behavioral tradeoffs in climate geoengineering pathways

Deliberations are underway to utilize increasingly radical technological options to help address climate change and stabilize the climatic system. Collectively, these options are often referred to as “climate geoengineering.” Deployment of such options, however, can create wicked tradeoffs in governance and require adaptive forms of risk management. In this study, we utilize a large and novel set of qualitative expert interview data to more deeply and systematically explore the types of risk–risk tradeoffs that may emerge from the use of 20 different climate geoengineering options, 10 that focus on carbon dioxide or greenhouse gas removal, and 10 that focus on solar radiation management and reflecting sunlight. We specifically consider: What risks does the deployment of these options entail? What types of tradeoffs may emerge through their deployment? We apply a framework that clusters risk–risk tradeoffs into institutional and governance, technological and environmental, and behavioral and temporal dimensions. In doing so, we offer a more complete inventory of risk–risk tradeoffs than those currently available within the respective risk-assessment, energy-systems, and climate-change literatures, and we also point the way toward future research gaps concerning policy, deployment, and risk management.     

基于产品生命周期的多阶段剩余收益项目决策模型

剩余收益模型（Residual Income Model,RIM）是评估公司权益价值的经典模型,特别地,奥尔森系列剩余收益模型由于可以利用历史财务与会计数据而得到广泛运用。本文在一般剩余收益模型的基础上,运用规范研究、逻辑推理等方法,基于产品生命周期理论,即项目处于产品生命周期的不同阶段时,其净资产收益率不同,在增长期的净资产收益率持续上升,衰退期的净资产收益率持续下降,而成熟期的净资产收益率则围绕行业平均水平波动,从理论上就项目投资价值的评估构建了多阶段剩余收益项目决策模型,并就所构建的多阶段剩余收益项目决策模型进行参数赋值分析。研究结果表明该新构建的决策模型具有较好的理论和运用价值,可以作为一种有效的项目投资决策模型。本文的研究一是拓展了剩余收益模型的应用,从主要用于对公司权益价值的估值拓展至对项目投资决策的评估;二是有利于寻求项目投资决策的价值最大化。     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.