Die Rendite der AHV sollte sich dem veränderten Produktionswachstum und der demografischen Entwicklung anpassen, die zweite Säule sollte sich strikt nach den Marktzinsen richten, schreibt Fabrizio Zilibotti.
We study from both a theoretical and an empirical perspective how a network of military alliances and enmities affects the intensity of a conflict. The model combines elements from network theory and from the politico-economic theory of conflict. We obtain a closed-form characterization of the Nash equilibrium. Using the equilibrium conditions, we perform an empirical analysis using data on the Second Congo War, a conflict that involves many groups in a complex network of informal alliances and rivalries. The estimates of the fighting externalities are then used to infer the extent to which the conflict intensity can be reduced through (i) dismantling specific fighting groups involved in the conflict; (ii) weapon embargoes; (iii) interventions aimed at pacifying animosity among groups. Finally, with the aid of a random utility model, we study how policy shocks can induce a reshaping of the network structure.
Optimal behavior requires striking a balance between exploiting tried-and-true options or exploring new possibilities. Neuroimaging studies have identified different brain regions in humans where neural activity is correlated with exploratory or exploitative behavior, but it is unclear whether this activity directly implements these choices or simply reflects a byproduct of the behavior. Moreover, it remains unknown whether arbitrating between exploration and exploitation can be influenced with exogenous methods, such as brain stimulation. In our study, we addressed these questions by selectively upregulating and downregulating neuronal excitability with anodal or cathodal transcranial direct current stimulation over right frontopolar cortex during a reward-learning task. This caused participants to make slower, more exploratory or faster, more exploitative decisions, respectively. Bayesian computational modeling revealed that stimulation affected how much participants took both expected and obtained rewards into account when choosing to exploit or explore: Cathodal stimulation resulted in an increased focus on the option expected to yield the highest payout, whereas anodal stimulation led to choices that were less influenced by anticipated payoff magnitudes and were more driven by recent negative reward prediction errors. These findings suggest that exploration is triggered by a neural mechanism that is sensitive to prior less-than-expected choice outcomes and thus pushes people to seek out alternative courses of action. Together, our findings establish a parsimonious neurobiological mechanism that causes exploration and exploitation, and they provide new insights into the choice features used by this mechanism to direct decision-making.
Conceptual priming has become an increasingly popular tool in economics. Here, we review the literature that uses priming in incentivized experiments to study economic questions. We mainly focus on the role of social identity, culture, and norms in shaping preferences and behavior. We also discuss recently raised objections to priming research and conclude with promising avenues for future research.
Certain estimation problems involving the covariance matrix in large dimensions are considered. Due to the breakdown of finite-dimensional asymptotic theory when the dimension is not negligible with respect to the sample size, it is necessary to resort to an alternative framework known as large-dimensional asymptotics. Recently, an estimator of the eigenvalues of the population covariance matrix has been proposed that is consistent according to a mean-squared criterion under large-dimensional asymptotics. It requires numerical inversion of a multivariate nonrandom function called the QuEST function. The numerical implementation of this QuEST function in practice is explained through a series of six successive steps. An algorithm is provided in order to compute the Jacobian of the QuEST function analytically, which is necessary for numerical inversion via a nonlinear optimizer. Monte Carlo simulations document the effectiveness of the code.
We develop a tractable dynamicmodel of productivity growth and technology spillovers that is consistent with the emergence of real world empirical productivity distributions. Firms can improve productivity by engaging in in-house R&D, or alternatively, by trying to imitate other firms’ technologies, subject to the limits of their absorptive capacities. The outcome of both strategies is stochastic. The choice between in-house R&Dand imitation is endogenous, and based on firms’ profit maximization motive. Firms closer to the technological frontier face fewer imitation opportunities, and choose in-house R&D, while firms farther from the frontier try to imitate more productive technologies. The equilibriumchoice leads to a balanced-growth equilibriumfeaturing persistent productivity differences even when starting from ex-ante identical firms. The long-run productivity distribution can be described as a traveling wave with tails following a Pareto as can be observed in the empirical data.
