“While the distribution of the richest 10% does indeed follow a different behavior (power law) than the rest (Gibbs or log-normal), one need not assume different dynamics at work in the two cases,” Chatterjee explained to PhysOrg.com. “In fact, both types of distributions can arise from the same model. In the case of the random savings model, the agents having the highest savings fractions will have a higher probability of ending up in the richest 10% of the population, while in the random thrift model, the agents with higher thrift value generally tend to be the richest.
Aside from these general models, the scientists also discovered some interesting details within their results. When comparing wealth (i.e. one’s net worth) with income, they found that wealth is much more unequally distributed than income (wealth models always have lower Pareto exponents, for any society). Also, while most of the data for the models is based on individuals, data from companies also seemed to follow the same models.