| The distribution of wealth displays remarkable regularities in its functional form over space and time, particularly in the upper tail that is distributed according to a power law. The high degree of complexity underlying the distribution of wealth in an economy does not allow us to explain the observed distribution by tracing the destinies of all involved agents. Instead, we take recourse to the maximum entropy formalism that originated in statistical mechanics and information theory---and was later generalized into a principle of probabilistic inference---in order to determine and economically interpret the aggregate constraints that are responsible for the observed wealth distribution. From a methodological point of view, our statistical equilibrium reasoning breaks with the prevalent conviction that macroeconomic outcomes must be explained and rooted in microeconomic behavior and, instead, argues that microeconomic outcomes are necessitated by macroeconomic constraints. In addition to a statistical equilibrium model of the power law distribution of wealth, we also present empirical evidence for the power law tail from a series of named subsets of the wealthiest U.S. individuals, together with estimates of the characteristic exponent of the distribution. More casual observations regarding the historical pattern of wealth distributions in a small sample of western countries, as well as a brief mobility analysis of the named subsets, complete the empirical part of our work. Finally we deal with calibration issues in the application of the theoretical model, and we also try to extend the statistical equilibrium theory of wealth over the entire support of the observed distribution. |
