Are there Social Spillovers in Consumers’ Security Assessments of Payment Instruments?

Even though security of payments has long been identified as an important aspect of the consumer payment experience, recent literature fails to appropriately assess the extent of social spillovers among payment users. We test for the existence and importance of such spillovers by analyzing whether social influence affects consumers’ perceptions of the security of payment instruments. Based on a 2008–2014 annual panel data survey of consumers, we find strong evidence of social spillovers in payment markets: others’ perceptions of security of payment instruments exert a positive influence on one’s own payment security perceptions. The significant and robust results imply that a consumer’s assessments of security converge to his peers’ average assessment: a 10 percent change in the divergence between one’s own security rating and peers’ average rating will result in a 7 percent change in one’s own rating in the next period. The results are robust to many specifications and do not change when we control for actual fraud or crime data. Our results indicate that spillovers rather than reflection appear to be the cause, although separating the two causes is very difficult (Manski 1993). In particular, the spillovers are stronger for people who experience an exogenous shock to security perception, people who have more social interactions, and younger consumers, who are more likely to be influenced by social media. We also examine the effects of social spillovers on payment behavior (that is, on decisions regarding payment adoption and use). Our results indicate that social spillovers have a rather limited impact on payment behavior, as others’ perceptions seem to affect one’s own payment behavior mainly indirectly through the effect on one’s own perceptions

The revolving door phenomenon revisited: time to readmission in 17'415 patients with 37'697 hospitalisations at a German hospital

Objective: Despite the recurring nature of the disease process in many psychiatric patients, individual careers and time to readmission rarely have been analysed by statistical models that incorporate sequence and velocity of recurrent hospitalisations. This study aims at comparing four statistical models specifically designed for recurrent event history analysis and evaluating the potential impact of predictor variables from different sources (patient, treatment process, social environment). Method: The so called Andersen-Gil counting process model, two variants of the conditional models of Prentice, Williams, and Peterson (gap time model, conditional probability model), and the so called frailty model were applied to a dataset of 17’415 patients observed during a 12 years period starting from 1996 and leading to 37’697 psychiatric hospitalisations. Potential prognostic factors stem from a standardized patient documentation form. Results: Estimated regression coefficients over different models were highly similar, but the frailty model best represented the sequentiality of individual treatment careers and differing velocities of disease progression. It also avoided otherwise likely misinterpretations of the impact of gender, partnership, historical time and length of stay. A widespread notion of psychiatric diseases as inevitably chronic and worsening could be rejected. Time in community was found to increase over historical time for all patients. Most important protective factors beyond diagnosis were employment, partnership, and sheltered living situation. Risky conditions were urban living and a concurrent substance use disorder. Conclusion: Prognostic factors for course of diseases should be determined only by statistical models capable of adequately incorporating the recurrent nature of psychiatric illnesses