新型农村社会养老保险参与决策中的同群效应

我国于2012年实现了社会保障在制度上的全覆盖。然而,对于自愿参与型社会保障项目,政策目标人群的参保率过低,无法充分发挥社会保障体系的风险分担和福利保障作用。2020年,党的十九届五中全会将健全社会保障体系作为“十四五”时期经济社会发展主要目标之一。分析政策目标人群的参保决策,有针对性地提高群众参保率,对于充分发挥社会保障制度的功能和健全社会保障体系具有重要意义。本文以新型农村社会养老保险(新农保)为例,使用家庭调查数据和工具变量方法,考察同群效应在社会保障项目参与决策中的作用。研究发现:同村居民参与新农保的比例每增加10个百分点,个体参保概率显著增加4.24个百分点;同村居民之间的信息传递以及其内部形成的社会规范是同群效应发挥作用的重要渠道;男性的参保行为具有更强的示范效应。本文的研究结论表明,加强基层政策宣传和提高政策透明度,以及有针对性地提高特定群体的参保率是提高社会保障项目整体参与率的有效手段。     

A picture is worth a thousand words: The role of a cover photograph on a travel agency’s online identity

Online travel agencies (OTAs) allow lodging businesses to select one cover photograph to represent itself on the OTA site. The cover photograph plays a crucial role for attracting customers’ attention from among alternatives, and lure viewers to view the webpage with detailed information. The present study investigates how the content of a business’ cover photograph on OTAs’ sites influences customers’ behavior when searching for information. The content of a cover photograph may fall within five categories according to attributes: façade, type of place, room amenities, scenery, and property amenities. Only façade and property amenities have positive impacts on customers’ viewing times of the webpages with the detailed information of businesses. In contrast, scenery has a negative influence on customers’ viewing times. The results of the study contribute, theoretically and methodologically, to OTAs’ knowledge base and can assist practitioners’ identification of effective cover photographs.     

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.     

Metaverse as a disruptive technology revolutionising tourism management and marketing

Metaverse is the next disruptive technology that will impact society in the coming decades, by enabling immersive experiences in both virtual and physical environments. Although still conceptual, Metaverse converges the physical and digital universe, allowing users to seamlessly traverse between them. Digital immersion offers opportunities for people to travel in time, supporting users to experience virtually ancient encounters, space explorations or dangerous natural phenomena, such as volcano eruptions. Users can explore immersive environments for working, learning, transacting, exploring interests and socialising with others. This is already evident in gaming ecosystems, where gamers effectively interact in the metaverse. Although still experimental, Metaverse is expected to revolutionize travel and tourism management and marketing. It empowers destination awareness, positioning and branding, as well as coordination and management, through digital twins. Metaverse provides opportunities to support trip planning, interaction and engagement, effectively transforming consumer behaviour. Visiting and engaging with destinations virtually is expected to motivate real travel, rather than replace it. This paper provides a vision of how Metaverse can revolutionize tourism experiences and transform tourism management and marketing. Drawing on a systematic review of scholarly works, articles from media and industry reports, this study defines and conceptualizes the Metaverse ecosystem for tourism and travel. It explores the foundations of the disruptions that Metaverse brings to tourism destinations and organisations and identifies the building blocks of Metaverse tourism. The study outlines research directions so that the tourism industry can take full advantage of the Metaverse capabilities and opportunities emerging as well as identify challenges for the future.     

Do significant risk warnings in annual reports increase corporate bond credit spreads? Evidence from China

Based on listed companies issuing bonds on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017, this study analyzes the relationship between significant risk warnings in Chinese companies’ annual reports and corporate bond credit spreads. The main findings are as follows. First, in the Chinese market, “substantial warnings of significant risks” can significantly improve corporate bond credit spreads, reflecting the risk-warning effect; second, state-owned property rights weaken this effect, which only pertains to listed companies with poor risk management and low information quality; third, significant risk warnings increase investors’ heterogeneous beliefs, also affecting credit spreads; and fourth, through textual analysis, it is found that the corporate bond credit spread is greater when the disclosed risk factors are more pessimistic and less similar to those of the previous year. The findings of this paper help to enrich the literature on credit spreads and risk disclosure.     

Natural resource rents and internal conflicts: Can decentralization lift the curse?

We study how natural resource rents affect the risk of internal conflict within countries and how the federal structure of countries influences this relationship. Natural resource abundance may induce excessive rent-seeking and thus increase the risk of internal conflict. Fiscal and political decentralization as an institutional arrangement for rent-sharing and political codetermination of regions within a country may limit the destructive effect of natural resource rents on internal stability. Using cross-country and panel data from more than 90 countries covering the period 1984–2004, we find evidence that natural resource rents indeed increase the risk of internal conflict, but this relationship is significantly mitigated by political decentralization.     

Use and misuse of information in supply chain forecasting of promotion effects

Demand forecasting is critical to sales and operations planning (S&OP), but the effects of sales promotions can be difficult to forecast. Typically, a baseline statistical forecast is judgmentally adjusted on receipt of information from different departments. However, much of this information either has no predictive value or its value is unknown. Research into base rate discounting has suggested that such information may distract forecasters from the average uplift and reduce accuracy. This has been investigated in situations in which forecasters were able to adjust the statistical forecasts for promotions via a forecasting support system (FSS). In two ecologically valid experiments, forecasters were provided with the mean level of promotion uplift, a baseline statistical forecast, and quantitative and qualitative information. However, the forecasters were distracted from the base rate and misinterpreted the information available to them. These findings have important implications for the design of organizational S&OP processes, and for the implementation of FSSs.     

Information flow between prediction markets,polls and media: Evidence from the 2008 presidential primaries

Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.