银行业联合授信制度制约企业的非效率投资了吗*——基于沪深A股公司的分析

中国银保监会出台联合授信管理制度旨在抑制企业过度融资行为，提高资金使用效率。为检验该制度是否达到预期效果，采用2014—2018年沪深A股上市公司样本，运用非平衡面板数据回归实证考察联合授信机制对企业非效率投资的影响，发现联合授信对企业的非效率投资（包含投资不足和投资过度）呈现显著抑制效应，其中联合授信对投资过度的抑制效应最大。进一步研究发现，相较于国有、大规模、低负债、高盈利等异质属性的企业，联合授信对非国有、小规模、高负债、低盈利等属性企业的非效率投资（尤其是投资过度）抑制效应更为显著。     

The effect of control deprivation on consumers’ adoption of no-pain,no-gain principle

Consumers often base their judgments on a no-pain, no-gain principle—that is, one must pay a cost in order to achieve a beneficial outcome. For example, they infer the quality of a product from its price and judge a bad-tasting medicine to be more effective than a tasty one. Although the use of this principle to infer the value of a product or service has been observed in several domains, the processes that underlie its use have not been fully explored. We find that when people feel out of control, they tend to use the principle because it exemplifies a causal relationship between actions and outcomes and endorsing it reaffirms their belief that they have control over the outcomes of their behavior. Our findings have implications for how marketers might position products and services to attract consumers who perceive themselves as having different levels of control.     

道德风险视域下职业经理人信用评价体系构建及测度：以重庆市为例

道德风险总是困扰着职业经理人群体，对企业发展产生不利影响。在实践中，作为建立健全社会信用体系的重要环节，职业经理人信用评价是防范职业经理人群体道德风险的基本思路和必要举措。本文基于马克思主义的理论启示，探索职业经理人三个维度的道德关系和道德风险，并围绕三个维度指向的个人信用、职业信用与职务信用等具体信用构成，阐述职业经理人信用评价内涵。进而，结合职业经理人信用评价内涵，构建系统应对道德风险的职业经理人信用评价体系，并以重庆为例进行实证测度。在此基础上，提出促进职业经理人道德意识与信用水平提升、实现新时代职业经理人群体高素质发展的对策建议。     

Shipbuilding administration under the Spanish Habsburg and Bourbon regimes (1590‒1834): A comparative perspective

Abstract

This article aims at understanding the evolution of the administrative methods devised by the Spanish monarchy to interact with regional private initiative for the production of warships. It also aims at understanding the practice of financing naval shipbuilding in the royal shipyards of Spain and Spanish America by public contribution, in the form of both credit and voluntary donations. These processes were largely influenced by the need of both political systems to perform efficiently in war. This article addresses the relationship between the state and local entrepreneurs and examines the reach and objectives of the Habsburg and Bourbon naval administrations, seen as variants of the ‘contractor state’.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Institutional Economics as Theory of Policy Change: Impact of Past Policy Failures on Present Policy

Social scientists have developed several theories for understanding or evaluating policy change over time. Since all costs or benefits are not internalized owing to positive transaction costs, policymaking is always implemented under cost underestimation conditions and, therefore, is imperfect. I call this trait policy failure in this article. Furthermore, I show that a new framework combining the social costs approach and the legal/economic approach in institutional economics is suitable and can be applied to evaluating how past policy failures affect present policy, providing as an example the Fukushima nuclear disaster in Japan.     

就业结构调整对水库移民土地流转的影响研究*——以南水北调中线工程移民为例

[目的]为促进水库移民的生计恢复及转型,探寻就业结构和土地流转间的交互机制,分析两者间耦合作用对当前移民安置模式及政策的影响。[方法]文章从移民就业结构变迁出发,通过超边际理论构建了安置区移民土地的流转机制模型,推演了从业决策对土地流转行为的影响路径,并运用南水北调农业安置移民数据进行实证检验。[结果]大面积转出土地是移民搬迁后土地处置的主要行为,移民中土地转入户有所增加,但土地集中度却有所减少。移民非农就业较搬迁前有明显上升,并有向当地兼业转移的趋势,但与土地流转间并不互为因果,农村社保还难以弱化其对土地养老的依赖。劳动力数量、非农就业时间、非农收入占比、土地政策等对移民土地流转行为和规模均有显著影响,而外出就业前景和地租水平在促进土地流转行为的同时,却不一定能增加土地流转面积。[结论]移民外迁中可以采用无土安置代替农业安置,推动移民土地流转的关键在于技能培训、政策保障和地租补贴,简单的就业推荐和土地权属划分的作用有限。而对于移民中的农业大户,则需在满足土地转入面积的基础上,保障其在安置区的各项土地权益。     

Farm size and productivity growth in the United States Corn Belt

In recent decades, agricultural production in the U.S. has continued to shift to large-scale operations, raising concerns about the economic viability of small and midsized farms. To understand whether economies of size provided an incentive for the consolidation of production, the study estimates the total factor productivity (TFP) of five size classes of grain-producing farms in the U.S. Heartland (Corn Belt) region. Using quinquennial Agricultural Census data from 1982 to 2012 the study also compares TFP growth rates across farm sizes to gain insight into whether observed productivity differences are likely to persist. The finding of a strong positive relationship between farm size and TFP suggests that consolidation of production has contributed to recent aggregate productivity growth in the crop sector. The study estimates the extent to which sectoral productivity growth can be attributed to structural change versus other factors including technological change. The study also explores some tradeoffs associated with policies that raise the productivity of small versus large farms.     

纳税信用评级结果披露与研发投入

以纳税信用评级披露作为一个天然外生冲击,并基于2013—2016年1214家A股上市公司的微观数据构造准自然实验,使用双重差分法系统评估纳税信用评级结果披露对上市公司研发投入的影响。结果发现:纳税信用评级结果披露显著增加了上市公司的研发投入。基于PSM-DID方法的估计结果与上述结论无明显差异。稳健性检验也表明上述结论的正确性。机制检验表明,纳税信用评级结果披露通过降低企业的融资约束,进而促进企业增加研发投入。此外,分样本回归发现纳税信用评级结果披露只能对中小型企业和民营企业的研发投入产生促进作用。