學術論文

We propose a new approach to modeling bond risk and risk premia, inspired by the equity risk-return literature, which does not impose the tight restrictions found in models that generate closed-form bond prices. We estimate the joint dynamics of the volatility and Sharpe ratio of principal-component bond-factor portfolios for the US and China. Predictors include yield curve variables and, for the US, VIX. We document complex time-varying relations between the price and quantity of interest rate risk inconsistent with the frameworks in existing studies. Interesting differences between the US and China further highlight the need for our more flexible approach.

This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a general and more realistic setup. Specifically, the comparison graph consists of hyper-edges of possible heterogeneous sizes, and the number of comparisons can be as low as one for a given hyper-edge. Such a setting is pervasive in real applications, circumventing the need to specify the graph randomness and the restrictive homogeneous sampling assumption imposed in the commonly used Bradley-Terry-Luce (BTL) or Plackett-Luce (PL) models. Furthermore, in scenarios where the BTL or PL models are appropriate, we unravel the relationship between the spectral estimator and the maximum likelihood estimator (MLE). We discover that a two-step spectral method, where we apply the optimal weighting estimated from the equal weighting vanilla spectral method, can achieve the same asymptotic efficiency as the MLE. Given the asymptotic distributions of the estimated preference scores, we also introduce a comprehensive framework to carry out both one-sample and two-sample ranking inferences, applicable to both fixed and random graph settings. It is noteworthy that this is the first time effective two-sample rank testing methods have been proposed. Finally, we substantiate our findings via comprehensive numerical simulations and subsequently apply our developed methodologies to perform statistical inferences for statistical journals and movie rankings.

Problem definition: As digitization transforms the service sector and empowers service platforms, questions arise about utilizing and disseminating customer information captured by digitization to enhance platform operations. We contribute by investigating how providing customer-related information at the start of a service encounter impacts both service supply and demand in the context of entertainment service platforms. Methodology/results: We conducted a field experiment on a live-streaming platform that connects hundreds of millions of viewers with individual broadcasters. For broadcasters randomly assigned to the treatment condition (but not for broadcasters in the control condition), when a viewer entered their shows, information about the viewer appeared on the screen. Our analyses, involving a random sample of 49,998 broadcasters, demonstrate that relative to control broadcasters, treatment broadcasters expanded service supply by 12.62% by increasing both show frequency (3.31%) and show length (7.10%), thus earning 10.44% more income, based on our conservative estimate. Moreover, our intervention increased service demand (measured by viewer watch time) by 4.51%. Additional analyses and surveys in our field setting and online experiments (n = 3,115) shed light on the potential mechanisms. Viewer-related information enables broadcasters to offer personalized service and vividly perceive viewers. On the demand side, viewers appreciate personalized service and interact with broadcasters more, which collectively boost demand. On the supply side, broadcasters not only enjoy the increased interaction with viewers but also feel a stronger sense of appreciation due to the more vivid mental image of viewers, which collectively lifts service supply. Managerial implications: This research suggests that providing customer-related information at the beginning of a service encounter can increase both service demand and supply. This low-cost, information-based intervention has important implications for digital service platforms that have little control over service providers’ work schedules and service quality.

Psychological empowerment plays a crucial role in motivating individuals to pursue their goals. This research explores a unique method to empower consumers in their goal pursuit: anthropomorphizing goals as dependent persons in need of care. Through three studies, including an incentive-compatible study and a field study conducted at a hypermarket, we demonstrate that viewing personal goals as dependent persons who require care and nurturance (e.g., presenting a goal as a dependent person in a health app) can significantly enhance goal pursuit (e.g., choosing healthy snacks) by boosting psychological empowerment. We find that this motivational boost does not occur when a goal is simply anthropomorphized as any person (Study 1), when it is depicted as a dependent object (Study 1), or when it is anthropomorphized as an independent person (Study 2). This research contributes to a deeper understanding of motivation in goal pursuit and offers practical implications for marketers. Specifically, by using marketing communications to frame goals as entities that require care, marketers can help individuals foster a more engaging and empowering relationship with their aspirations.

Although everyone must make decisions about where to live, we know little about how consumers perceive social interactions with neighbors and incorporate information about neighbors into their residence decisions. This article explores the impact of consumers' self-construal on the importance they attribute to neighbor information when choosing a residence. Across seven preregistered studies, including lab experiments and online surveys, the findings consistently show that independent consumers, compared with interdependent ones, place greater importance on information about potential neighbors. This tendency stems from independent consumers' stronger desire for social closeness with neighbors, prompting them to place more weight on information that will help them find neighbors with whom they could become friends.

The rapid development of blockchain has inspired many traditional centralized intermediaries to transform their transaction models, especially for the peer-to-peer market. Lately, the token-based (blockchain) system (with cryptocurrency) is gaining popularity. However, little is known about the (comparative) performance of different operating types. In this study, we build an analytical framework to find the optimal strategies for the token-based and nontoken-based blockchain (as a special application scenario) platforms and derive the essential model properties and characteristics. We analytically show how the optimal mining bonus depends on the fraction of reserved tokens sold to customers and on the price-to-sales ratio. Furthermore, we obtain several actionable findings for choosing suitable platform types under different scenarios. The shift from the nontoken-based platform to the token-based platform may yield greater social welfare unless the nontoken-based system operates with a much larger ride price, which we show to be unrealistic for the considered Beijing case through numerical studies. Moreover, we find that the matching probability for the token-based platform is predominantly higher than that for the nontoken-based one. Besides, government interventions may encourage a path toward a fair consensus mechanism or a high decentralization level in order to enhance social welfare. One unanticipated finding is that a higher decentralization level may lead to a lower mining capacity shortage and so to a more efficient system, indicating that the combination of blockchain and the sharing economy has much potential.

This paper uses three decades of data on rhesus monkeys to investigate the multigenerational effects of early-life advantage. Monkeys and their offspring are both randomly assigned to be reared together or apart from their mothers. For the first time, we document the intergenerational complementarity of early-life advantage, where the benefits of maternal rearing are present only for offspring of mothers who were themselves mother reared. This illustrates the intergenerational dynamics of social advantage and how the value of an intervention can depend on the experiences of the previous generation. Our paper demonstrates how studies of primates can inform human development.

With in-feed advertising becoming an increasingly popular advertising tool for advertisers to reach mobile consumers, the authors propose an integrated model of contemporaneous, carryover, and spillover effects to measure the incremental contributions of in-feed ads in multiple types of mobile apps: newsfeed, social, and video. They empirically examine the three proposed effects of newsfeed ads, social feed ads, and video feed ads using data from a large-scale ad campaign for a new mobile game. The data set contains 10,115,801 impressions, 286,506 clicks, and 12,706 conversions. First, the findings show that social feed ads have the strongest contemporaneous effects on both ad clicks and conversions; social feed ads are 1.87 times more likely to generate a click and 1.69 times more likely to generate a conversion than newsfeed ads, followed by video feed ads (clicks: 1.73 times; conversions: 1.55 times). Second, video feed ads have the strongest carryover effect, followed by social feed ads, while newsfeed ads have a negative carryover effect. Third, newsfeed ads exhibit the strongest spillover effect; prior newsfeed ad exposures are more effective than prior social or video feed ad exposures at promoting clicks and conversions upon subsequent exposure in other channels.

Using a new database on global multinational production (MP), I document that world multinational enterprise (MNE) sales declined as sharply as trade during the Great Recession (2008–2009). This collapse was driven by MNEs from a few key headquarters countries and associated with steeper GDP declines in MP-intensive countries. MNEs amplified the trade collapse because their overall sales fell while they maintained higher trade intensity than domestic firms. In a calibrated quantitative model with flexible vertical and horizontal MNE structures, international trade, and input–output linkages, I show that productivity shocks, which disproportionately affected trade-intensive MNEs, contributed more to the trade collapse than demand shocks. MNEs’ productivity shocks accounted for over half of the global GDP decline during the Great Recession. MP linkages significantly amplified the transmission of headquarters-country productivity shocks to global GDP, MP, and trade.