Effects of the turnover rate on the size distribution of firms: An application of the kinetic exchange models

We address the issue of the distribution of firm size. To this end we propose a model of firms in a closed, conserved economy populated with zero-intelligence agents who continuously move from one firm to another. We then analyze the size distribution and related statistics obtained from the model. There are three well known statistical features obtained from the panel study of the firms i.e., the power law in size (in terms of income and/or employment), the Laplace distribution in the growth rates and the slowly declining standard deviation of the growth rates conditional on the firm size. First, we show that the model generalizes the usual kinetic exchange models with binary interaction to interactions between an arbitrary number of agents. When the number of interacting agents is in the order of the system itself, it is possible to decouple the model. We provide exact results on the distributions which are not known yet for binary interactions. Our model easily reproduces the power law for the size distribution of firms (Zipf’s law). The fluctuations in the growth rate falls with increasing size following a power law (though the exponent does not match with the data). However, the distribution of the difference of the firm size in this model has Laplace distribution whereas the real data suggests that the difference of the log of sizes has the same distribution.     

An analysis of the size distribution of Italian firms by age

In this paper we analyze the size distribution of Italian firms by age. In other words, we want to establish whether the way that the size of firms is distributed varies as firms become old. As a proxy of size we use capital. In [L.M.B. Cabral, J. Mata, On the evolution of the firm size distribution: Facts and theory, American Economic Review 93 (2003) 1075–1090], the authors study the distribution of Portuguese firms and they find out that, while the size distribution of all firms is fairly stable over time, the distributions of firms by age groups are appreciably different. In particular, as the age of the firms increases, their size distribution on the log scale shifts to the right, the left tails becomes thinner and the right tail thicker, with a clear decrease of the skewness. In this paper, we perform a similar analysis with Italian firms using the CEBI database, also considering firms’ growth rates. Although there are several papers dealing with Italian firms and their size distribution, to our knowledge a similar study concerning size and age has not been performed yet for Italy, especially with such a big panel.     

Spatial characteristics of joint application networks in Japanese patents

Technological innovation has extensively been studied to make firms sustainable and more competitive. Within this context, the most important recent issue has been the dynamics of collaborative innovation among firms. We therefore investigated a patent network, especially focusing on its spatial characteristics. The results can be summarized as follows. (1) The degree distribution in a patent network follows a power law. A firm can then be connected to many firms via hubs connected to the firm. (2) The neighbors’ average degree has a null correlation, but the clustering coefficient has a negative correlation. The latter means that there is a hierarchical structure and bridging different modules may shorten the paths between the nodes in them. (3) The distance of links not only indicates the regional accumulations of firms, but the importance of time it takes to travel, which plays a key role in creating links. (4) The ratio of internal links in cities indicates that we have to consider the existing links firms have to facilitate the creation of new links.     

The firm as a bundle of barcodes

We empirically investigate the firm growth model proposed by Buldyrev et al. by using a unique dataset that contains the daily sales of more than 200 thousand products, which are collected from about 200 supermarkets in Japan over the last 20 years. We find that the empirical firm growth distribution is characterized by a Laplace distribution at the center and power-law at the tails, as predicted by the model. However, some of these characteristics disappear once we randomly reshuffle products across firms, implying that the shape of the empirical distribution is not produced as described by the model. Our simulation results suggest that the shape of the empirical distribution stems mainly from the presence of relationship between the size of a product and its growth rate.     

Production, depreciation and the size distribution of firms

Many empirical researches indicate that firm size distributions in different industries or countries exhibit some similar characters. Among them the fact that many firm size distributions obey power-law especially for the upper end has been mostly discussed. Here we present an agent-based model to describe the evolution of manufacturing firms. Some basic economic behaviors are taken into account, which are production with decreasing marginal returns, preferential allocation of investments, and stochastic depreciation. The model gives a steady size distribution of firms which obey power-law. The effect of parameters on the power exponent is analyzed. The theoretical results are given based on both the Fokker-Planck equation and the Kesten process. They are well consistent with the numerical results.     

Robust Characterization of Multidimensional Scaling Relations between Size Measures for Business Firms

Although the sizes of business firms have been a subject of intensive research, the definition of a “size” of a firm remains unclear. In this study, we empirically characterize in detail the scaling relations between size measures of business firms, analyzing them based on allometric scaling. Using a large dataset of Japanese firms that tracked approximately one million firms annually for two decades (1994–2015), we examined up to the trivariate relations between corporate size measures: annual sales, capital stock, total assets, and numbers of employees and trading partners. The data were examined using a multivariate generalization of a previously proposed method for analyzing bivariate scalings. We found that relations between measures other than the capital stock are marked by allometric scaling relations. Power–law exponents for scalings and distributions of multiple firm size measures were mostly robust throughout the years but had fluctuations that appeared to correlate with national economic conditions. We established theoretical relations between the exponents. We expect these results to allow direct estimation of the effects of using alternative size measures of business firms in regression analyses, to facilitate the modeling of firms, and to enhance the current theoretical understanding of complex systems.     

Changes of firm size distribution: The case of Korea

In this paper, the distribution and inequality of firm sizes is evaluated for the Korean firms listed on the stock markets. Using the amount of sales, total assets, capital, and the number of employees, respectively, as a proxy for firm sizes, we find that the upper tail of the Korean firm size distribution can be described by power-law distributions rather than lognormal distributions. Then, we estimate the Zipf parameters of the firm sizes and assess the changes in the magnitude of the exponents. The results show that the calculated Zipf exponents over time increased prior to the financial crisis, but decreased after the crisis. This pattern implies that the degree of inequality in Korean firm sizes had severely deepened prior to the crisis, but lessened after the crisis. Overall, the distribution of Korean firm sizes changes over time, and Zipf’s law is not universal but does hold as a special case.     

Firm size distribution and mobility of the top 500 firms in China,the United States and the world

This paper considers the macroscopic and microscopic statistical features of the top 500 firms in China, the United States and the world, denoted as China 500 (CH500), Fortune 500 (US500) and Fortune Global 500 (FG500). From a macroscopic perspective, the firm size distribution of each category, when measured by revenue, is steadily distributed over the observed period, even during periods of financial crises. As is evidenced by the Gini coefficient, divergences between firm scales are most significant for the CH500. From a microscopic perspective, the underlying micro-dynamics are volatile and often turbulent due to the exit and entry of firms as well as shifts in their revenues and ranks. Such fluctuations, or mobility, are visualized in rank/revenue/share clocks. We also propose a revenue/rank/share mobility index that is a quantitative measurement of mobility. Among these, we find that the share mobility acts as an effective indicator of economic status; where there is a share mobility spike, there is an ailing economy. The share mobility indexes indicate that the 2008 Financial Crisis had little impact on the Chinese economy, while it triggered violent changes in the top 500 firms in the United States and the world.     

Shape of growth-rate distribution determines the type of Non-Gibrat’s Property

In this study, the authors examine exhaustive business data on Japanese firms, which cover nearly all companies in the mid- and large-scale ranges in terms of firm size, to reach several key findings on profits/sales distribution and business growth trends. Here, profits denote net profits. First, detailed balance is observed not only in profits data but also in sales data. Furthermore, the growth-rate distribution of sales has wider tails than the linear growth-rate distribution of profits in log–log scale. On the one hand, in the mid-scale range of profits, the probability of positive growth decreases and the probability of negative growth increases symmetrically as the initial value increases. This is called Non-Gibrat’s First Property. On the other hand, in the mid-scale range of sales, the probability of positive growth decreases as the initial value increases, while the probability of negative growth hardly changes. This is called Non-Gibrat’s Second Property. Under detailed balance, Non-Gibrat’s First and Second Properties are analytically derived from the linear and quadratic growth-rate distributions in log–log scale, respectively. In both cases, the log-normal distribution is inferred from Non-Gibrat’s Properties and detailed balance. These analytic results are verified by empirical data. Consequently, this clarifies the notion that the difference in shapes between growth-rate distributions of sales and profits is closely related to the difference between the two Non-Gibrat’s Properties in the mid-scale range.     

The origin of asymmetric behavior of money flow in the business firm network

In the business firm network, the number of in-degrees and out-degrees show the same scale-free property, however, the distribution of authorities and hubs show asymmetric behavior. Here we show the result of an analysis of the two-link structure of the network to find the origin of this asymmetric behavior. We find the tendency for big construction firms intermediating small subcontracting firms to have higher hub degrees. By measuring the strength of preferential attachment rate of new companies, we also find a abnormally strong preferential attachment for which the exponent is 1.4 with respect to out-degree when a new company forms a business partnership with a construction company. We propose a new model that reproduces the asymmetric behavior of the degrees of authorities and hubs by changing the preferential attachment rate between the in-degree and the out-degree in the business firm network.     

Firm profitability and the network of organizational capabilities

A Laplace distribution for firm profit rates (or returns on assets) can be obtained through the sum of many independent shocks if the number of shocks is Poisson distributed. Interpreting this as a linear chain of events, we generalize the process to a hierarchical network structure. The hierarchical model reproduces the observed distributional patterns of firm profitability, which crucially depend on the life span of firms. While the profit rates of long-lived firms obey a symmetric Laplacian, short-lived firms display a different behavior depending on whether they are capable of generating positive profits or not. Successful short-lived firms exhibit a symmetric yet more leptokurtic pdf than long-lived firms. Our model suggests that these firms are more dynamic in their organizational capabilities, but on average also face more risk than long-lived firms. Finally, short-lived firms that fail to generate positive profits have the most leptokurtic distribution among the three classes, and on average lose slightly more than their total assets within a year.     

Role of parties in the vote distribution of proportional elections

We analyze proportional election data to show the influence of parties on the results of this kind of election. The study compiles data from different countries and dates to show that depending on how the candidate’s votes are counted, one can find that these votes have different distributions. When considering the fraction of votes received by the candidates, the vote distribution has a power law behavior with exponent α=1 for all cases studied. However, this universal behavior is modified when we normalize the fraction of votes by the mean number of votes of the candidate’s party. Considering this normalization, the Brazilian and the Finnish results are now different. The former follows an exponential while the latter a log-normal distribution.     

A Gibbs-Like Measure for Single-Time, Multi-Scale Energy Transfer in Stochastic Signals and Shell Model of Turbulence

A Gibbs-like approach for simultaneous multi-scale correlation functions in random, time-dependent, multiplicative processes for the turbulent energy cascade is investigated. We study the optimal log-normal Gibbs-like distribution able to describe the subtle effects induced by non-trivial time dependency on both single-scale (structure functions) and multi-scale correlation functions. We provide analytical expression for the general multi-scale correlation functions in terms of the two-point correlations between multipliers and we show that the log-normal distribution is already accurate enough to reproduce quantitatively many of the observed behavior. The main result is that non-trivial time effects renormalize the Gibbs-like effective potential necessary to describe single-time statistics. We also present a generalization of this approach to more general, non log-normal, potentials. In the latter case one obtains a formal expansion of both structure functions and multi-scale correlations in terms of cumulants of all orders.     

Collective firm bankruptcies and phase transition in rating dynamics

We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends on the mean interaction strength. For small interaction strength parameters, there are many independent bankruptcies of individual companies. For large parameters, there are giant collective defaults of firm clusters. In the case when the individual firm dynamics favors dumping of rating changes, there is an optimal strength of the firm’s interactions from the systemic risk point of view.     

A generalized preferential attachment model for business firms growth rates

We present a preferential attachment growth model to obtain the distribution P(K) of number of units K in the classes which may represent business firms or other socio-economic entities. We found that P(K) is described in its central part by a power law with an exponent ϕ = 2+b/(1-b) which depends on the probability of entry of new classes, b. In a particular problem of city population this distribution is equivalent to the well known Zipf law. In the absence of the new classes entry, the distribution P(K) is exponential. Using analytical form of P(K) and assuming proportional growth for units, we derive P(g), the distribution of business firm growth rates. The model predicts that P(g) has a Laplacian cusp in the central part and asymptotic power-law tails with an exponent ζ = 3. We test the analytical expressions derived using heuristic arguments by simulations. The model might also explain the size-variance relationship of the firm growth rates.     

Large-scale structure of a nation-wide production network

Production in an economy is a set of firms’ activities as suppliers and customers; a firm buys goods from other firms, puts value added and sells products to others in a giant network of production. Empirical study is lacking despite the fact that the structure of the production network is important to understand and make models for many aspects of dynamics in economy. We study a nation-wide production network comprising a million firms and millions of supplier-customer links by using recent statistical methods developed in physics. We show in the empirical analysis scale-free degree distribution, disassortativity, correlation of degree to firm-size, and community structure having sectoral and regional modules. Since suppliers usually provide credit to their customers, who supply it to theirs in turn, each link is actually a creditor-debtor relationship. We also study chains of failures or bankruptcies that take place along those links in the network, and corresponding avalanche-size distribution.     

Multifractal structure of fully developed hydrodynamic turbulence. I. Kolmogorov's third hypothesis revisited

We discuss intermittency effects in fully developed hydrodynamic turbulence. It is shown that the application of the bounded log-normal distribution to the fluctuations of the local energy dissipation rate resolves some basic difficulties related to Kolmogorov's third hypothesis and gives a good agreement with experiment. The nonlinear interaction of the large-scale and inertial-range turbulent pulsations of the velocities may explain the observable characteristics of the intermittency. We give also a detailed comparison of the results obtained with the use of the bounded log-normal distribution with that obtained in the framework of the homogeneous and random-models, a two-scale Cantor set approximation, and the original unbounded log-normal distribution suggested by Kolmogorov and Obukhov.     

Particle sizing in concentrated suspensions by use of steady-state, continuous-wave photon-migration techniques

We demonstrate a new approach for determining particle-size distribution in concentrated suspensions from spectral measurement of isotropic scattering coefficients by use of steady-state, continuous-wave photon-migration techniques. Successful recovery of particle-size distribution for TiO(2) suspensions in the form of log-normal functions is achieved through a regularized inverse algorithm, into which a synthesized scheme of Marquardt and Tikhonov regularizations has been incorporated. Our results for dense TiO(2) suspensions with three different particle concentrations are in excellent agreement with the size distribution as measured with x-ray sedimentation.     

Quasi-Self-Preserving Log-Normal Size Distributions in the Transition Regime

A log-normal model, based on the extended SIMPLER-algorithm utilizing the Modal Aerosol Dynamics Modelling Technique, and a sectional model are compared for Brownian coagulation in the transition regime. The models are in good agreement with respect to the calculated particle size distribution moments. Applying the log-normal model a simple coagulation model consisting of a closed set of five equations is developed that does not require the solution of ordinary diferential equations.     

Experimental evidence for the interplay between individual wealth and transaction network

We conduct a market experiment with human agents in order to explore the structure of transaction networks and to study the dynamics of wealth accumulation. The experiment is carried out on our platform for 97 days with 2,095 effective participants and 16,936 times of transactions. From these data, the hybrid distribution (log-normal bulk and power-law tail) in the wealth is observed and we demonstrate that the transaction networks in our market are always scale-free and disassortative even for those with the size of the order of few hundred. We further discover that the individual wealth is correlated with its degree by a power-law function which allows us to relate the exponent of the transaction network degree distribution to the Pareto index in wealth distribution.