Economies of scale in the US computer industry: An empirical investigation using data envelopment analysis

Abstract

Up to recently, economists have had no good tools to measure the returns to scale of individual corporations in an industry. Data envelopment analysis (DEA) is a linear programming technique for determining the efficiency frontier (the envelope) to the inputs and outputs of a collection of individual corporations or other productive units. While DEA offers an avenue for calculating the returns to scale of individual corporations, the approach has been riddled by mathematical complications arising from the possibility of alternate optima. The present paper develops theory for calculating the entire range of these alternate optima. Furthermore, in a quite ambitions empirical application, DEA is employed to determine the time path of returns to scale of all publicly held U.S. computer companies over the time period 1980–1991. For the great majority of companies, a unique time path is obtained; only in less than 4 percent of the linear programming calculations is an entire range of alternate optima obtained. The results indicate that the computer industry was polarized into two camps: large aging corporations with decreasing returns to scale, and swarms of small upstart companies with advanced technology exhibiting increasing returns to scale.