Zipf's law in income distribution of companies

Okuyama, K; Takayasu, Misako; Takayasu, Hideki

doi:10.1016/s0378-4371(99)00086-2

Cited by 233 publications

(172 citation statements)

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“…Though recognize the heuristic nature of CDF and PDF methods. Okuyama et al (1999) provide evidence from a sample of Japanese firms and from a crosscountry dataset, also disaggregating the analysis by industrial sector of activity. The variable under study is firm income before taxes, and data are pooled over 4 years for Japanese firms and over 7 years for the other countries.…”

Section: Ols-rankmentioning

confidence: 99%

“…Conversely, they are not meaningful when studying the distribution of firm sizes over a yearly cross-section of firms. However, controlling for an autoregressive structure can indeed be important when the behavior of the firm size distribution is inferred from pooled crosssections (as in Okuyama et al, 1999), so that a firm is observed repeatedly over time. We deal with this issue in the next section.…”

Section: T-tests Power Under Sub-asymptotic Deviation From Zipf Lawmentioning

confidence: 99%

“…1 Introduction and identically distributed, but rather exhibit dependence over time, a case which is likely to arise when pooling together different years of data on firm sizes (as done, e.g., in Okuyama et al, 1999). In agreement with the vast literature on Gibrat's law in firm size dynamics, we simulate panel samples with an autoregressive structure and Laplace growth shocks, and check the distortion this generates in the upper tail behaviour of the pooled distribution.…”

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confidence: 93%

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Zipf law and the firm size distribution: a critical discussion of popular estimators

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThe upper tail of the firm size distribution is often assumed to follow a Power Law behavior. Recently, using different estimators and on different data sets, several papers conclude that this distribution follows the Zipf Law, that is that the fraction of firms whose size is above a given value is inversely proportional to the value itself. We compare the different methods through which this conclusion has been reached. We find that the family of estimators most widely adopted, based on an OLS regression, is in fact unreliable and basically useless for appropriate inference. This finding rises some doubts about previously identified Zipf Laws. In general, when individual observations are available, we recommend the adoption of the Hill estimator over any other method.JEL codes: L11; C15; C46; D20

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Section: Ols-rankmentioning

confidence: 99%

Section: T-tests Power Under Sub-asymptotic Deviation From Zipf Lawmentioning

confidence: 99%

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confidence: 93%

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Zipf law and the firm size distribution: a critical discussion of popular estimators

2015

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“…• Initial amount of money: According to [11][12][13][14], it is likely that the Zipf's law is quite universal. On the light of this observation, we have decided to distribute the money to traders following such a distribution.…”

Section: The Modelmentioning

confidence: 99%

“…Differently from the threshold, the initial wealth of the traders follows a Zipf's distribution. Inspired by [11][12][13][14] we have decided to distribute the richness according to a Zipf's law in the following way: the 20% of the traders posses around the 80% of M and among the two groups this rule is applied again in a recursive way 2 . In this way we are able to model the difference between a normal agent and an istitutional investor and the different effect they produce when they decide to enter the market.…”

Section: Simulation Runsmentioning

confidence: 99%

On financial markets trading

Matassini

Franci

2001

Physica A: Statistical Mechanics and its Applications

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Abstract.Starting from the observation of the real trading activity, we propose a model of a stockmarket simulating all the typical phases taking place in a stock exchange. We show that there is no need of several classes of agents once one has introduced realistic constraints in order to confine money, time, gain and loss within an appropriate range. The main ingredients are local and global coupling, randomness, Zipf distribution of resources and price formation when inserting an order. The simulation starts with the initial public offer and comprises the broadcasting of news/advertisements and the building of the book, where all the selling and buying orders are stored. The model is able to reproduce fat tails and clustered volatility, the two most significant characteristics of a real stockmarket, being driven by very intuitive parameters.

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