It is generally recognized that there is a high degree of economic concentration in the United States and elsewhere.1 The term economic concentration refers to the extent to which an industry's assets, output, or sales are distributed among the firms in that industry. Economic concentration frequently results in a distribution of assets in an industry that is similar to log normal distributions. In other words, a few firms hold a large share of an industry's assets while many firms hold small shares of the assets. Such skewed distributions are readily observable in the steel, banking, and automobile industries to name a few. Log normal distributions have been replicated by a variety of simulation techniques which has led some observers to conclude that market structure may be determined stochastically. This article uses a Monte Carlo simulation growth process and a financial model developed by Lerner and Carleton to assess the relationships between selected financial variables and economic concentration. The variables are financial leverage, rate of return on assets, interest rates, proportion of earnings retained, and income tax rates. As an outgrowth of the simulations, it was also possible to examine some financial characteristics of the fastest and slowest growing firms in a hypothetical industry. The article is divided into four parts. Part one defines two measures of economic concentration and simulates a growth process. In Part two, growth is measured by a financial model, and Monte Carlo simulations are used to examine the impact of growth on economic concentration. The third part presents selected financial characteristics of the fastest growth firms and those that are growing at a slower rate. The final part concerns some institutional factors. [ABSTRACT FROM AUTHOR]