Managing leverage of infrastructure projects: Aggregate and sectoral risk effect

Funding for infrastructure projects recently shows that debt has a portion more than equity, triggering public debates. Therefore, leverage as an instrument to measure the ability and willingness of project sponsors to fund becomes the utmost importance to discuss. Relating to leverage, risk and government participation are two main factors that can explain the choice of funding decisions by the project sponsors. For this reason, this study would analyze the effect of risk and government participation on leverage through the two main sectors of infrastructure projects, namely the transportation sector and the energy sector, and derivating risk to political risk and financial risk. The objects of research were 976 infrastructure projects listed in the Asian Development Bank during 2007−2016. We use censored regression to examine the model by infrastructure sectors, both as individual and through interaction effects. The analysis showed that overall, leverage of infrastructure projects was rather influenced by financial risk than by political risk. However, the leverage of infrastructure projects in the transportation sector was more vulnerable to risk than that in other sectors.     

Does local government competition aggravate haze pollution? A new perspective of factor market distortion

Haze pollution has become a new threat to China's sustainable development, but it may be that local government behaviour can play an important role in the prevention and control of pollutants. A dynamic spatial autoregressive (SAR) model is used to study the relationship between local government competition and haze pollution. To further explore the indirect impact of factor market distortion on haze pollution and control potential endogeneity problems, a newly developed intermediary effect model that incorporates the characteristics of the generalized method of moments (GMM) is utilized to explore how factor market distortion indirectly affects haze pollution. The research results show that regional haze pollution in China is characterized by significant spatial correlation, and local government competition has a positive impact on haze pollution; that is, local government competition exacerbates haze pollution. In general, local government competition not only directly leads to an increase in haze pollution but also further intensifies it by distorting the local factor market, and the intermediary role of factor market distortion is approximately 7.04%. The results of the regional inspection found that competition among local governments in the eastern region did not lead to haze pollution, and distortion of the factor market did not exist as an intermediary effect. However, both direct and intermediary effects are significant in the central and western regions. Therefore, an official performance appraisal system that includes ecological constraints should be established to guide the benign transformation of local government competition, and an environmental management mechanism must be developed for joint prevention and control to reduce haze pollution. In addition, the free flow of factors and marketization are equally important.     

Larger,counter-intuitive and lasting – The PSM role in responding to the COVID-19 pandemic,exploring opportunities for theoretical and actionable advances

PSM has played an important role in the initial mitigation of risks caused by the COVID-19 pandemic. We explore the nature and scope of this role and develop a roadmap for PSM contributions towards greater supply chain resilience. We find that the role of PSM is (1) multidimensional; responding to supply risks but also to demand and logistics risks, (2) counter to game theory; collaboration increased instead of decrease, (3) multi-stage; beyond the initial response the hardest change efforts are still ahead. The pandemic is accelerating the journey towards future-proof PSM but not necessarily revolutionizing the future of PSM.     

国家高新区设立能够提升城市全要素生产率吗？——基于261个地级市TFP分解数据的路径分析

国家高新区是培育经济增长新动能，实现区域经济高质量发展的重要政策载体，文章利用2001-2014年我国261个城市面板数据，通过渐进双重差分法研究国家高新区设立对城市全要素生产率的影响。研究发现：研究期内国家高新区的设立对城市全要素生产率造成显著的负面影响；从TFP的分解项来看，高新区设立促进了所在城市规模效率提高，却抑制了技术进步和技术效率提高；异质性分析结果表明上述作用具有显著城市等级异质性，在高行政等级城市设立高新区对城市全要素生产率具有促进作用。进一步运用工业企业数据库、微观土地转让数据、城市专利数据等进行机制分析发现，企业间资源错配和土地价格扭曲降低了技术效率，而技术创新下降和产业升级钝化是抑制技术进步的主要机制。     

金融资产配置与违约风险:蓄水池效应,还是逐利效应？

本文以2007—2016年中国A股上市公司为研究对象,检验了企业金融资产配置对违约风险的影响。实证研究发现:金融资产持有量越多,企业的违约风险越低,金融资产配置的“蓄水池效应”显著;在货币政策宽松时期,金融资产配置导致的代理冲突显现,宽松的货币政策会抑制金融资产投资对违约风险的降低作用。政府规制也会有一定的公司治理作用,将产业政策纳入讨论发现:对于产业政策支持的行业来说,企业金融资产配置能够降低违约风险,但是宽松的货币政策会刺激管理层的短视投资行为,抑制政府规制的公司治理作用。进一步地,本文提出会计稳健性的提升是企业金融资产配置降低违约风险的重要路径。本文的研究结论丰富了企业金融资产配置动机和违约风险影响因素的讨论,能够为政府部门防范经济运行中的内在风险提供有益借鉴。     

Analysis of territorial compatibility for Seveso-type sites using different risk assessment methods and GIS technique

The increasing size of chemical plants and rapid growth in residential areas has led to many incompatible land-use scenarios in the last 100 years. In this context, the authors assume that assessment and planning have their significant role in preventing the juxtaposition of hazards and population, and that, in this field, there is a broad recognition of the need for legislative and policy consistency across the European Union. The paper presents a comparative case study in which the Romanian land-use planning (LUP) criteria and the risk-based quantitative approach for a chemical plant are applied. Accident scenarios involving chlorine and propylene arecomprehensively analyzed using consequence and risk modelling software and GIS technique for the territorial compatibility assessment. The objective of the paper is threefold. Firstly, it presents an overview about current risk analysis methods; secondly, the authors advance an understanding of risk assessment practices used in several countries for the prevention and control of major industrial accidents involving dangerous substances and, also, for LUP. Thirdly, a method targeting an improved risk assessment framework for LUP, encompassing Romania’s determinants is outlined. The results obtained using the two different approaches indicate significant differences regarding the possibly affected areas and territorial compatibility. Furthermore, based on the findings, the paper ends with a set of recommendations that can be transformed into the foundation for future enactments of new safety standards that cover risk assessment for LUP. Consequently, the present study aims to become a frame of reference for decision-makers towards more sustainable and updated risk assessment practices in the field of industrial activities.     

要素自由流动能实现区域协调发展吗——基于“协调性集聚”的理论假说与实证检验

经济活动的空间集聚是当代经济内涵式增长的源泉,现有文献多从要素流动视角来考察经济集聚过程中的区域差异。然而,若将要素流动引入区域生产函数进行推导,可以发现非区域性要素集中规模的扩大,既能提高要素流入区域的劳动生产率,也能提高要素流出区域的要素收益率,基于此,本文以“协调性集聚”为核心概念,通过拓展现代集聚模型来构建要素在自由流动中走向协调发展的理论假说。实证结果进一步显示,虽然我国发展要素在大规模流向东部发达地区,并呈现进一步向超大城市和城市群集聚的趋势,但要素流动在提高要素流入区域劳动生产率的同时,也显著提高了要素流出区域的要素收益率,并从需求侧激发了要素流出区域的市场潜能,要素流动在要素配置效率不断提高的过程中呈现“协调性集聚”的趋势。因此,发达的要素流入区域通过优化公共服务体系提高劳动生产率,欠发达的要素流出区域通过建立健全要素自由流动机制提高要素回报率和收益率,是新时代我国形成市场主导型区域协调发展机制的有效途径。     

资产透明度、监管套利与银行系统性风险

资产不透明的金融机构过度依赖批发性融资进行监管套利不利于系统性风险的防控。在此背景下,本文首先在经典银行道德风险模型的基础上引入关联性,从资产透明度和监管套利的视角分析银行系统性风险累积的内在机理。而后利用2007-2018年中国上市银行微观数据,构建资产透明度指标和系统性风险指标(SRISK、MES),对理论推论进行实证检验。主要结论有:(1)资产不透明、监管套利会提高银行的系统性风险。(2)监管套利弱化了资产透明度和资本监管机制对银行系统性风险承担的约束作用,资产透明度与资本监管机制在约束系统性风险承担中的协调作用不明显。(3)以大银行为主的债权银行受监管套利的影响相较于受资产透明度的影响更明显。在此基础上,我们对完善金融风险防范体系以及监管机制提出了若干建议。     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.