International audienceWithin a general equilibrium framework a la Long and Plosser (1983), we investigate the dynamics emerging from the interactions of households and firms that are adaptive price setters and financially constrained. Adaptive price-setting behavior induces micro founded out-of-equilibrium dynamics along which agents become heterogeneous in terms of prices and wealth. The stringency of the financial constraints determine the regime into which the model settles: either an equilibrium one or a disequilibrium one conductive to financial fragility and aggregate volatility. In this setting , we investigate how the structure of the production network a↵ects the emergence of aggregate volatility from micro-level price and financial shocks, hence providing a dynamical counterpart to recent results of Acemoglu and al (2012)

Credit market, Price fluctuations, Market interactions, Interest rate dynamics, Nonlinear dynamics, C02, C6, E4,

The paper provides a survey of results related to the "κ-generalized distribution", a statistical model for the size distribution of income and wealth. Topics include, among others, discussion of basic analytical properties, interrelations with other statistical distributions as well as aspects that are of special interest in the income distribution field, such as the Gini index and the Lorenz curve. An extension of the basic model that is most able to accommodate the special features of wealth data is also reviewed. The survey of empirical applications given in this paper shows the κ-generalized models of income and wealth to be in excellent agreement with the observed data in many cases.

Economic agents differ from physical atoms because of the learning capability and memory, which lead to strategic behaviour. Economic agents learn how to interact and behave by modifying their behaviour when the economic environment changes. We show that business fluctuations are endogenously generated by the interaction of learning agents via the phenomenon of regenerative-coordination, i.e. agents choose a learning strategy which leads to a pair of output and price which feedback on learning, possibly modifying it. Mathematically, learning is modelled as a chemical reaction of different species of elements, while inferential analysis develops combinatorial master equation, a technique, which is an alternative approach in modelling heterogeneous interacting learning agents.

This article documents that the Gini index is an insufficient measure of inequality and, according to the traditional logic of interpretation, that it may lead to incorrect deductions. Since, apart from concentration, it cannot grasp other relevant features of inequality like heterogeneity and asymmetry-which, beyond its intensity, allow for considering the direction of inequality too-we suggest using the less known Zanardi index of asymmetry of the Lorenz curve as an appropriate measure of inequality. Our findings are supported with estimates from the Luxembourg Income Study Database.