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We propose a unified framework to measure the effects of different reforms of the pension system on retirement ages and macroeconomic indicators in the face of demographic change. A rich overlapping generations (OLG) model is built and endogenous retirement decisions are explicitly modeled within a public pension system. Heterogeneity with respect to consumption preferences, wage profiles, and survival rates is embedded in the model. Besides the expected direct effects of these reforms on the behavior of households, we observe that feedback effects do occur. Results suggest that individual retirement decisions are strongly influenced by numerous incentives produced by the pension system and macroeconomic variables, such as the statutory eligibility age, adjustment rates, the presence of a replacement rate, and interest rates. Those decisions, in turn, have several impacts on the macro-economy which can create feedback cycles working through equilibrium effects on interest rates and wages. Taken together, these reform scenarios have strong implications for the sustainability of pension systems. Because of the rich nature of our unified model framework, we are able to rank the reform proposals according to several individual and macroeconomic measures, thereby providing important support for policy recommendations on pension systems.
We develop a model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. We show that when dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. Our empirical analysis supports the model's predictions.
Augmented reality (AR) gained much public attention since the success of Pok´emon Go in 2016. Technology companies like Apple or Google are currently focusing primarily on mobile AR (MAR) technologies, i.e. applications on mobile devices, like smartphones or tablets. Associated privacy issues have to be investigated early to foster market adoption. This is especially relevant since past research found several threats associated with the use of smartphone applications. Thus, we investigate two of the main privacy risks for MAR application users based on a sample of 19 of the most downloaded MAR applications for Android. First, we assess threats arising from bad privacy policies based on a machine-learning approach. Second, we investigate which smartphone data resources are accessed by the MAR applications. Third, we combine both approaches to evaluate whether privacy policies cover certain data accesses or not. We provide theoretical and practical implications and recommendations based on our results.
This paper provides an assessment framework for privacy policies of Internet of Things Services which is based on particular GDPR requirements. The objective of the framework is to serve as supportive tool for users to take privacy-related informed decisions. For example when buying a new fitness tracker, users could compare different models in respect to privacy friendliness or more particular aspects of the framework such as if data is given to a third party. The framework consists of 16 parameters with one to four yes-or-no-questions each and allows the users to bring in their own weights for the different parameters. We assessed 110 devices which had 94 different policies. Furthermore, we did a legal assessment for the parameters to deal with the case that there is no statement at all regarding a certain parameter. The results of this comparative study show that most of the examined privacy policies of IoT devices/services are insufficient to address particular GDPR requirements and beyond. We also found a correlation between the length of the policy and the privacy transparency score, respectively.
This paper investigates the roles psychological biases play in empirically estimated deviations between subjective survival beliefs (SSBs) and objective survival probabilities (OSPs). We model deviations between SSBs and OSPs through age-dependent inverse S-shaped probability weighting functions (PWFs), as documented in experimental prospect theory. Our estimates suggest that the implied measures for cognitive weakness, likelihood insensitivity, and those for motivational biases, relative pessimism, increase with age. We document that direct measures of cognitive weakness and motivational attitudes share these trends. Our regression analyses confirm that these factors play strong quantitative roles in the formation of subjective survival beliefs. In particular, cognitive weakness is an increasingly important contributor to the overestimation of survival chances in old age.
Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
We prove the existence of an equilibrium in competitive markets with adverse selection in the sense of Miyazaki (1977), Wilson (1977), and Spence (1978) when the distribution of unobservable risk types is continuous. Our proof leverages the finite-type proof in Spence (1978) and a limiting argument akin to Hellwig (2007)’s study of optimal taxation.
Telemonitoring devices can be used to screen consumer characteristics and mitigate information asymmetries that lead to adverse selection in insurance markets. Nevertheless, some consumers value their privacy and dislike sharing private information with insurers. In a secondbest efficient Miyazaki-Wilson-Spence (MWS) framework, we allow consumers to reveal their risk type for an individual subjective cost and show analytically how this affects insurance market equilibria as well as social welfare. We find that information disclosure can substitute deductibles for consumers whose transparency aversion is sufficiently low. This can lead to a Pareto improvement of social welfare. Yet, if all consumers are offered cross-subsidizing contracts, the introduction of a screening contract decreases or even eliminates cross-subsidies. Given the prior existence of a cross-subsidizing MWS equilibrium, utility is shifted from individuals who do not reveal their private information to those who choose to reveal. Our analysis informs the discussion on consumer protection in the context of digitalization. It shows that new technologies challenge cross-subsidization in insurance markets, and it stresses the negative externalities that digitalization has on consumers who are unwilling to take part in this
development
Inhibitory interneurons govern virtually all computations in neocortical circuits and are in turn controlled by neuromodulation. While a detailed understanding of the distinct marker expression, physiology, and neuromodulator responses of different interneuron types exists for rodents and recent studies have highlighted the role of specific interneurons in converting rapid neuromodulatory signals into altered sensory processing during locomotion, attention, and associative learning, it remains little understood whether similar mechanisms exist in human neocortex. Here, we use whole-cell recordings combined with agonist application, transgenic mouse lines, in situ hybridization, and unbiased clustering to directly determine these features in human layer 1 interneurons (L1-INs). Our results indicate pronounced nicotinic recruitment of all L1-INs, whereas only a small subset co-expresses the ionotropic HTR3 receptor. In addition to human specializations, we observe two comparable physiologically and genetically distinct L1-IN types in both species, together indicating conserved rapid neuromodulation of human neocortical circuits through layer 1.