Inelastically scattering particles and wealth distribution in an open economy

Slanina, František

doi:10.1103/physreve.69.046102

Cited by 164 publications

(184 citation statements)

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“…conservation of wealth during the exchange process (2) and the existence of a stationary solution. This is in contrast to many previous models formulated in different contexts where 'wealth' may be either lost Krapivsky (2002); Ben-Avraham (2003); Bobylev (2000); Baldassarri (2002) or gained Slanina (2004) in the exchange process and the distribution function has a power-law-tail only in an asymptotic sense.…”

Section: Theoretical Analysiscontrasting

confidence: 43%

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Dynamics of money and income distributions

Repetowicz

Hutzler

Richmond

2005

Physica A: Statistical Mechanics and its Applications

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We study the model of interacting agents proposed by Chatterjee (2003) that allows agents to both save and exchange wealth. Closed equations for the wealth distribution are developed using a mean field approximation.We show that when all agents have the same fixed savings propensity, subject to certain well defined approximations defined in the text, these equations yield the conjecture proposed by Chatterjee (2003) for the form of the stationary agent wealth distribution.If the savings propensity for the equations is chosen according to some random distribution we show further that the wealth distribution for large values of wealth displays a Pareto like power law tail, ie P (w) ∼ w 1+a . However the value of a for the model is exactly 1. Exact numerical simulations for the model illustrate how, as the savings distribution function narrows to zero, the wealth distribution changes from a Pareto form to to an exponential function. Intermediate regions of wealth may be approximately described by a power law with a > 1. However the value never reaches values of ∼ 1.6 − 1.7 that characterise empirical wealth data. This conclusion is not changed if three body agent exchange processes are allowed. We conclude that other mechanisms are required if the model is to agree with empirical wealth data.

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Section: Theoretical Analysiscontrasting

confidence: 43%

“…The renewed interest by physicists and mathematicians in econo-and sociophysics has however led to publication of a number of new papers on the topic in recent years (see Slanina (2004) for an extensive literature review).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of money and income distributions

Repetowicz

Hutzler

Richmond

2005

Physica A: Statistical Mechanics and its Applications

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“…This is a characteristic of a multiplicative stochastic process, where the changes in the value of a variable are proportional to the value, rather than an additive process, where the changes are independent of the value (e.g., random walks). This lends support to the assumptions of asset exchange models for wealth distribution [2,3,4,5,6,7], according to which, the amount lost or gained by agents through each trading interaction is a random fraction of their wealth at a given instant. The data, although of low resolution, is suggestive of a log-normal distribution in the low-to middle-income range.…”

Section: Resultsmentioning

confidence: 91%

“…The occurrence of a qualitatively similar distribution across widely differing geographical regions and economic development stages may be indicative of universal features of inequality in human societies. This has led to attempts at developing simple models for generating wealth distributions that are qualitatively similar to those empirically observed, with asset exchange interactions between agents [2,3,4,5,6,7]. To verify such models further empirical measurements of wealth distribution in different economies is essential.…”

Section: Introductionmentioning

confidence: 99%

Evidence for power-law tail of the wealth distribution in India

Sinha

2006

Physica A: Statistical Mechanics and its Applications

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The higher-end tail of the wealth distribution in India is studied using recently published lists of the wealth of richest Indians between the years 2002-4. The resulting rank distribution seems to imply a power-law tail for the wealth distribution, with a Pareto exponent between 0.81 and 0.92 (depending on the year under analysis). This provides a comparison with previous studies of wealth distribution, which have all been confined to Western advanced capitalist economies. We conclude with a discussion on the appropriateness of multiplicative stochastic process as a model for asset accumulation, the relation between the wealth and income distributions (we estimate the Pareto exponent for the latter to be around 1.5 for India), as well as possible sources of error in measuring the Pareto exponent for wealth.

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“…For example, kinetic-type equations have been introduced in order to describe a simple market economy with a constant growth mechanism [5,6,16], showing the formation of steady states with (overpopulated) Pareto tails.…”

Section: Introductionmentioning

confidence: 99%