Gender differences in willingness to compete: The role of public observability

A recent literature emphasizes that gender differences in the labor market may in part be driven by a gender gap in willingness to compete. However, whereas experiments in this literature typically investigate willingness to compete in private environments, real world competitions often have a more public nature, which introduces potential social image concerns. If such image concerns are important, and men and women differ in the degree to which they want to be seen as competitive, making tournament entry decisions publicly observable may further exacerbate the gender gap. We test this prediction using a laboratory experiment (N = 784) that varies the degree to which the decision to compete, and its outcome, is publicly observable. We find that public observability does not alter the magnitude of the gender gap in willingness to compete in an economically or statistically significant way.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

当代资本主义经济停滞问题:制度结构与治理危机——基于积累的社会结构理论的分析

关于经济停滞问题的探讨是对马克思主义经济危机理论的传承。2008年金融危机爆发十余年,资本主义经济停滞趋于常态化。积累的社会结构理论从资本积累与支撑这一积累的制度环境之间的矛盾出发,发现了主流经济学探讨资本主义经济停滞所忽略的制度性因素。积累的社会结构学派沿着"中间层次"的路径,将2008年金融危机界定为系统性的结构危机,认为新自由主义SSA的失灵是此次经济停滞持续的原因。资本主义经济停滞引发欧美发达资本主义国家的社会治理危机升级,一系列政治、社会和文化危机不断加剧。积累的社会结构理论从微观层面深刻把握了资本主义制度转型的轨迹,但并未深入探讨开展根本的制度变革。在面临百年未有之大变局的时代境遇下,中国在参与全球治理的过程中不断探索并推动世界经济政治新秩序朝着更加公正、合理的方向发展。     

The value of being precise

This study investigates how individuals assess imprecise information. We focus on two essential dimensions of decision under uncertainty, outcomes and probabilities, and their respective precision. We believe the precision of information is highly relevant in the investment setting, as reflected in the well-known “home (familiarity) bias”, and the outcome and probability dimensions, separately or jointly, may affect investors’ knowledge of uncertainty and perceived risk of the investment options, and subsequently affect investors’ choices. To test this conjecture, we conducted three experiments. Our results show that 1) participants demonstrate a pattern of preference for precision and aversion of extreme vagueness and associate vagueness with higher perceived risk and lower investment (experiments one and two); 2) participants prefer vague outcome information to vague probability information (experiment two); 3) familiarity indeed positively affects the precision of estimated values, but this association is stronger for the outcome dimension than for probabilities (experiment three). Our results confirm that precision in information, especially in the outcome dimension has an impact on investors’ resource allocation choices.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

The impact of social power and influence on the implementation of innovation strategies: A case study of a UK mega infrastructure construction project

Influence plays a key role in reaching consensus among multiple actors involved in project-based decision-making processes. While prior literature devotes considerable attention to describing influence, little attention has been paid to influence at the individual level of the strategic project manager within the context of megaprojects. This research intended to fill this knowledge gap by identifying and describing the influence strategies that a strategic project manager applies when implementing innovation strategies on megaprojects. A qualitative case study was used to examine the complex social processes involved in a major UK capital investment programme. The findings underline a critical subset of influence strategies, notably higher-management support, inspirational appeal and bargaining. The study proposes a utilitarian structure of social power comprising selective, supportive and executory power bases.     

Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

市场分割、政企关系与异地市场拓展

中国的市场仍旧存在某种程度的分割，如何突破这种市场分割到异地拓展市场是企业要考虑的现实问题。以前的研究主要是从省级层面或国家层面进行宏观分析。文章创新地从政企关系视角入手来考察市场分割情形下微观层面的企业异地市场拓展，也是首先把市场分割（和异地市场）分为省内（本市外）市场、省外市场和海外市场三个不同层面来研究的论文。用世界银行的调查数据从微观企业个体层面检验了企业异地市场拓展的可能。实证研究结果发现，市场分割确实阻碍了企业异地市场拓展，但这仅限于省内市场而不是省外市场。此外，市场分割是有方向性的，从地方保护主义严重的市场里走出来的企业更容易拓展异地市场，但此结论仅适用于国内市场拓展而不适用于海外市场拓展。良好的政企关系是有利于企业异地市场拓展的，但仅限于省外市场，对省内市场以及海外市场是无效的。文章解释了其中的原因，并分析了对于企业实践的启示。