Returns to Scale in the U. S. Life Insurance Industry

Abstract

This study explores the average cost relationship between life insurers and each of ten insurer characteristics. The data used are drawn from 90 insurers with more than half the life insurance in force with U.S. insurers. Evidence shows that the following independent variables are significant in producing economies of scale, premium income, new business ratio, proportion of whole life business, and size of insurer (i.e., giant mutual insurers). This study seeks evidence of economies of scale for a sample of 90 U.S. insurers. Data are obtained from the 1977 Life Insurance Superintendent's Report for the New York State Insurance Department. The Statistical Tables give data for 1976. The plan of this paper is to present a brief survey of the existing research and literature on this subject, to discuss the implications of estimating cost curves from life insurance data, to examine the data and the results of the estimated cost curves, and to offer a brief conclusion. The New York tables contain data for 67 New York life insurers, 52 from other states, and 2 from Canada. The gross premium income of these insurers less reinsurance is $39.24 billion compared with total U.S. gross premium income less reinsurance in 1976 of $66.38 billion. Therefore, these data represent 59 percent of the total life premium income. However, some of these insurers must be excluded from the sample in order to obtain a clearer picture of the economies of scale in the life insurance industry. The result is that the remaining insurers in the sample have an income of $34.8 1 billion, or 52.5 percent of the total rather than 59 percent. Recent Literature and Research on Economics of Scale Among Life Insurers Geehan [4] estimates a long-run average cost curve for the Canadian life insurance industry using an efficiency measure based on a weighted sum of activities. He argues that previous studies of returns to scale have used biased proxies for measurement of expenses which have lead to exaggerated estiPeter Praetz, Ph.D., F.I.A., is Professor of Finance and Econometrics, Monash University, Clayton, Victoria, 3168, Australia. Dr. Praetz was on leave at the Graduate School of Management, University of Rochester, when this study was made. Particular thanks are due to S. Travis Pritchett, who offered many helpful comments on an earlier draft of this article.