Power laws in firm size and openness to trade: Measurement and implications

Giovanni, Julian di; Levchenko, Andrei A.; Rancière, Romain

doi:10.1016/j.jinteco.2011.05.003

Cited by 133 publications

(137 citation statements)

References 20 publications

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“…di Giovanni and Levchenko (2010) study the universe of French firms in 2006, while di Giovanni et al (2011) analyze a large set of countries from the AMADEUS-ORBIS database.…”

Section: Ols-rankmentioning

confidence: 99%

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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“…di Giovanni and Levchenko (2010) study the universe of French firms in 2006, while di Giovanni et al (2011) analyze a large set of countries from the AMADEUS-ORBIS database.…”

Section: Ols-rankmentioning

confidence: 99%

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

2015

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“…Meanwhile, the common point of departure under the hypothesis of Zipf's law is to assume that the firm size distribution is well described by a Pareto or power-law distribution above a certain minimum threshold [21] [15] [16] [22]. In this manner, if we seek to study the growth of smaller firms compared to that of larger firms, then we cannot use a Pareto or power-law distribution because the small firms are found in the upper tail, below the threshold value [2] [23].…”

Section: Introductionmentioning

confidence: 99%

Measuring firm size distribution with semi-nonparametric densities

Cortés

Mora–Valencia

Perote

2017

Physica A: Statistical Mechanics and its Applications

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In this article, we propose a new methodology based on a (log) semi-nonparametric (log-SNP) distribution that nests the lognormal and enables better fits in the upper tail of the distribution through the introduction of new parameters. We test the performance of the lognormal and log-SNP distributions capturing firm size, measured through a sample of US firms in [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015]. Taking different levels of aggregation by type of economic activity, our study shows that the log-SNP provides a better fit of the firm size distribution. We also formally introduce the multivariate log-SNP distribution, which encompasses the multivariate lognormal, to analyze the estimation of the joint distribution of the value of the firm's assets and sales. The results suggest that sales are a better firm size measure, as indicated by other studies in the literature.

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“…Even though bank sizes can follow a power law in a model with endogenous markups, the size distribution might not be sufficently disperse. This is because our truncated efficiency distribution for banks necessarily has a finite variance, unlike the standard singly truncated Pareto distribution used in Gabaix (2011) and Di Giovanni et al (2011). In those studies, the singly truncated Pareto distribution of efficiency yields a power law distribution of firm size with infinite variance, such that the Central Limit Theorem gives way to the Lévy theorem.…”

Section: Does Granularity Hold?mentioning

confidence: 99%

“…Maximum likelihood estimates reveal tails for the banking sector in the world's largest economies that are fatter than those found for manufacturing firms by Di Giovanni et al (2011). 4 These patterns in the data suggest that shocks hitting large banks may indeed have aggregate effects.…”

Section: Introductionmentioning

confidence: 97%

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Big Banks and Macroeconomic Outcomes: Theory and Cross-Country Evidence of Granularity

et al. 2013

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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. The banking sector for many countries is indeed granular, as the right tail of the bank size distribution follows a power law. We then demonstrate granular effects in the banking sector on macroeconomic outcomes. The presence of big banks measured by high market concentration is associated with a positive and significant relationship between bank-level credit growth and aggregate growth of credit or gross domestic product. Terms of use: Documents in

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Power laws in firm size and openness to trade: Measurement and implications

Cited by 133 publications

References 20 publications

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

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

Measuring firm size distribution with semi-nonparametric densities

Big Banks and Macroeconomic Outcomes: Theory and Cross-Country Evidence of Granularity

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