Linking thoughts to feelings: Investigating cognitive appraisals and consumption emotions in a mixed-emotions context

While emotions have been shown to have significant influence on various consumer behaviors, the cognitive appraisals linked to consumption emotions have not been fully explored. This research investigates how individuals' cognitive interpretations of situations correspond to the emotions they experience in these contexts. Using analysis of variance and multiple discriminant analysis, our results show a systematic relationship between cognitive appraisals and 10 consumption emotions. The author's findings offer theoretical insights into these consumption emotions, the appraisal/consumption emotion correspondence, basisversus subordinate-level category differences in emotions, and mixed consumption emotions. The findings provide a practical framework for academics and practitioners interested in better understanding and managing consumption emotions. Julie A. Ruth (Ph.D., University of Michigan) is an assistant professor of marketing in the School of Business—Camden at Rutgers University. Her research interests include affect and emotions, consumer relationships, and consumer response to brand strategies such as brand alliances and sponsorships. Frédéric F. Brunel (Ph.D., University of Washington) is an assistant professor of marketing in the School of Management at Boston University. His research interests include consumer perceptions of product design and aesthetics, consumer attitude and affect, and gender and sociocultural issues in consumption. Cele C. Otnes (Ph.D., University of Tennessee) is an associate professor of business administration in the College of Commerce and Business Administration at the University of Illinois, Urbana. Her research interests include consumer rituals, affect and consumer ambivalence, and gift exchange.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

Endogenous structural change and crisis in a multiple time-scales growth model

The slow and endogenous twist of economic macro-structure makes up an important evolutionary feature of capitalist economies, and may be at the root of structural crisis. In this line, a Goodwinian growth model with increasing returns and profit-sharing that tries to picture a simple scenario of the seventies crisis is considered. It is shown that the exhaustion of the Kaldor-Verdoorn “productivity law” can entail, in a nonlinear framework, a “catastrophic” bifurcation from a “high” to a “low” growth path. Slow/fast dynamical systems then allow one to formalize a multiple time-scales dynamics where the growth path is shaped by the structural framework in which it takes place, but has also a long -un feedback. Structural change and crisis appear as long term and endogenous outcomes.     

How useful are signals? A micro-structure analysis

We analyze how the market processes a signaling event by studying a sample of self-tender offers, events often viewed as signals of firm value. By examining changes in the degree of informed trading, we find asymmetric information costs fall at announcement, remain low throughout the event, and increase at offer expiration. By one month following expiration, informed trading returns to a level not significantly different from that prior to the offer. Higher risk firms have significantly larger declines in information asymmetry during the offer. Increases in information asymmetry persist one month following expiration for firms with lower pre-offer informed trading. (JEL G14, G32)     

A review and validation of overall survival extrapolation in health technology assessments of cancer immunotherapy by the National Institute for Health and Care Excellence: how did the initial best estimate compare to trial data subsequently made available?

Background: Validation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.

Methods: A systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.

Results: The review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4–2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.

Conclusion: Long-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.     

Component efficient solutions in line-graph games with applications

Recently, applications of cooperative game theory to economic allocation problems have gained popularity. We investigate a class of cooperative games that generalizes some economic applications with a similar structure. These are the so-called line-graph games being cooperative TU-games in which the players are linearly ordered. Examples of situations that can be modeled like this are sequencing situations and water distribution problems. We define four properties with respect to deleting edges that each selects a unique component efficient solution on the class of line-graph games. We interpret these solutions and properties in terms of dividend distributions, and apply them to economic situations. This research has been done while the third author was visiting Tinbergen Institute at the Free University, Amsterdam. The research is part of the Research Programme “Strategic and Cooperative Decision Making” at the Department of Econometrics. Financial support from the Netherlands Organization for Scientific Research (NWO) in the framework of the Russian-Dutch programme for scientific cooperation, is gratefully acknowledged. The third author also appreciates partial financial support from the Russian Leading Scientific Schools Fund (grant 80.2003.6) and Russian Humanitarian Scientific Fund (grant 02-02-00189a). We thank three anonymous referees for their valuable comments.     

Introduction to “Economic and Financial Governance in the European Union after a decade of Economic and Political Crises.”

ABSTRACT

This paper offers the Introduction to the Special Issue “Economic and Financial Governance in the European Union after a decade of Economic and Political Crises.” We introduce the five papers. We distill three important lessons they offer for EU integration.     

The Geopolitics of Protected Areas

ABSTRACT

The conservation enterprise is embedded in ideas of the environment through which it promotes a vision of the world and the relations between the non-human and human. The papers in this forum analyse conservation from various vantage points to draw the links between geopolitics and conservation. The authors use three themes to demonstrate these links. The first theme draws on the concept of environmentality to show the mobilization of ecological rationalities and power towards the creation of protected areas. The second pays attention to networks formed across the distance, and how they influence the location and governance of protected areas. The third focuses on the strategies the conservation lobby uses to align local identities with global conservation ideals and goals. Collectively, these themes highlight features of conservation geopolitics.     

Why Punish? Social reciprocity and the enforcement of prosocial norms

Recently economists have become interested in why people who face social dilemmas in the experimental lab use the seemingly incredible threat of punishment to deter free riding. Three theories with evolutionary microfoundations have been developed to explain punishment. We survey these theories and use behavioral data from surveys and experiments to show that the theory called social reciprocity in which people punish norm violators indiscriminately explains punishment best.JEL Classification: C91, C92, D64, H41 Correspondence to: Jeffrey P. CarpenterWe thank Carolyn Craven, Corinna Noelke and two referees for comments, and Middlebury College for financial assistance. In addition, Carpenter acknowledges the support of the National Science Foundation (SES-CAREER 0092953).