{"title":"Inequality measures in wealth exchange models based on saving propensity","authors":"Xiang Lu, Xudong Wang","doi":"10.1016/j.physa.2025.131057","DOIUrl":null,"url":null,"abstract":"<div><div>Wealth inequality has gradually become a widely discussed and researched topic. Building upon existing researches related to saving propensity and kinetic models of wealth distribution, we propose the implicit saving propensity (ISP) model and the explicit saving propensity (ESP) model. We obtain the steady state wealth distributions as well as the Gini and <span><math><mi>k</mi></math></span>-indices of the ISP and ESP models through simulations. We find that the ISP model produces very limited types of wealth distribution shapes and yields relatively large Gini indices, whereas the ESP model, after incorporating saving behavior, is able to generate more diverse results, including producing power-law tails, and the Pareto exponent exhibits robustness for some specific parameters. The Gini index of the ESP model is also compared with empirical data from countries around the world and can effectively cover them. This indicates that, after incorporating saving behavior, the explanatory power and realistic value of our model have been significantly enhanced. Moreover, the mixture of Gamma distribution is used to fit the wealth distributions with different parameters, which shows the excellent agreement between simulation and theoretical results. Based on the mechanism of saving propensity, the ISP and ESP models are effective to reveal the wealth inequality qualitatively and quantitatively, which have flexible applicability in modeling the wealth dynamics in the real-world.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"680 ","pages":"Article 131057"},"PeriodicalIF":3.1000,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437125007095","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Wealth inequality has gradually become a widely discussed and researched topic. Building upon existing researches related to saving propensity and kinetic models of wealth distribution, we propose the implicit saving propensity (ISP) model and the explicit saving propensity (ESP) model. We obtain the steady state wealth distributions as well as the Gini and -indices of the ISP and ESP models through simulations. We find that the ISP model produces very limited types of wealth distribution shapes and yields relatively large Gini indices, whereas the ESP model, after incorporating saving behavior, is able to generate more diverse results, including producing power-law tails, and the Pareto exponent exhibits robustness for some specific parameters. The Gini index of the ESP model is also compared with empirical data from countries around the world and can effectively cover them. This indicates that, after incorporating saving behavior, the explanatory power and realistic value of our model have been significantly enhanced. Moreover, the mixture of Gamma distribution is used to fit the wealth distributions with different parameters, which shows the excellent agreement between simulation and theoretical results. Based on the mechanism of saving propensity, the ISP and ESP models are effective to reveal the wealth inequality qualitatively and quantitatively, which have flexible applicability in modeling the wealth dynamics in the real-world.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.