The Estimation of Age/Earnings Profiles in Wrongful Death and Injury Cases

Abstract

This study deals with the use of age/earnings profiles in wrongful death and injury cases. Previous studies have used the income growth rates for age cohorts, although it is well known that differences in the education level, the race, the sex, and the occupation of an individual affect the rate of growth in earnings in a systematic fashion. The focus of the study is to use Census data to determine the amount that each socio-economic variable affects earnings, and use this individual-specific informaton to generate specific age/earnings profiles. Introduction This Journal has focused a good deal of attention to the estimation of economic loss in wrongful death and injury cases. Two issues capture essentially the degree of difficulty inherent in determining the compensation to those involved in wrongful death or injury cases. They are: (1) estimating the growth rate of earnings, and (2) determining the discount rate used to derive the present value of lifetime earnings. Though the second issue is of interest in itself, this study deals only with issues associated with the estimation of the growth rate of lifetime earnings. Previous approaches have involved using the growth rates of age cohorts [1] or, as Larsen and Marten [2] have done, keyed on earnings growth due to experience, inflation, and productivity. This procedure generates very broad growth rates, however, which the labor economics literature would suggest are not accurate. For example, the age/earnings profiles of blacks tend to be flatter than those of whites [3], those of women flatter than those of men, those of 'secondary' labor market workers flatter than those of 'primary' labor Julia I. Lane and Dennis C. Glennon are both Assistant Professors in the University of Louisville Economics Department. Both received their Ph.D.s from the University of Missouri. *The authors acknowledge the contribution of James Marlin, Western Illinois University to this study. This project was partially funded by the University of Louisville Graduate School. This content downloaded from 157.55.39.116 on Sat, 11 Jun 2016 05:26:26 UTC All use subject to http://about.jstor.org/terms The Estimation of Age/Earnings Profiles 687 market workers, and so on. Similarly, education and human capital levels affect the shape of age/earnings profiles [4]. This suggests, then, that a more detailed analysis may be useful in generating growth patterns of earnings. Specifically, this paper uses an earnings function that has been used extensively in the labor economics literature [5], and that can be used to estimate the growth of lifetime earnings suited to the specific characteristics of the individual involved. In other words, it is possible to look at the Census earnings of individuals with similar labor market characteristics, but of different ages. This information may be used to pinpoint the expected growth in any individual's earnings over time. Thus, if a worker is currently earning $14,000 per year, and a similar worker who is ten years older is earning $18,000 per year, the best guess of what the first worker will be making in ten years is $18,000. Since no two workers are exactly identical, this paper will use regression analysis to find the contribution of each separate productivity characteristic to earnings. Briefly, this study will estimate an earnings equation using cross sectional, 1980 Census data. The current earnings of individuals of different ages will be used to estimate any one individual's future earnings. The advantage of using a cross-sectional study is that it reduces any inflationary bias in the data, and also reduces the effects of business cycle fluctuations on earnings. The disadvantage is that one is not able to be as precise as in a longitudinal survey, but a large number of similar studies use cross-section data which helped a choice of data base. The structure of the study is as follows. First, the paper discusses the model and the data then represents the empirical results. Three examples of age/ earnings profiles of different individuals are generated so that their growth rates may be contrasted.