16-17 year olds, or both, we estimate that these differentials would increase total teenage employment. amount of the increase is largely conjectural, since error variances in estimated parameters are large. For example, we estimate a two-standard-deviation upper bound for increased teenage employment of roughly 5% for a differential of 1.60-1.28 extended to the 14-15 year-old group, and an upper bound increase of 10% if the same differential is extended to all those 14-17 years old. lower bound in each case is roughly zero, so that a reduction in total teenage employment seems unlikely. An important question of differentials is the effect on employment of those groups to whom the differential is not extended. For a differential extended to 14-15 year-old workers, we estimate virtually no effect on employment of those 16-19 years old. substitution effects are nullified by scale effects. For a more broadly based 1.601.28 differential extended to those 14-17, the range of uncertainty concerning employment of those 18-19 is large, encompassing reductions and increases of 5%. REFERENCES Allen, R.G.D., Mathematical Analysis for Economists (New York: St. Martins Press, 1938). Hashimoto, Masanori, and Jacob Mincer, Employment and Effects of unpublished manuscript, National Bureau of Economic Research, April 1970. Kosters, Marvin, and Finis Welch, The Effects of Wages on the Distribution of Changes in Aggregate Employment, American Economic Review 62 (June 1972), 323-332. Mincer, Jacob, Unemployment Effects of Journal of Political Economy 84, part 2 (Aug. 1976), 87-104. Parsons, Donald, The Cost of School Time, Foregone Earnings, and Human Capital Formulation, unpublished manuscript, Ohio State University, Feb. 1973. Siskind, Frederic B., Minimum Wage Legislation in the United States: Comment, Economic Inquiry 15 (Jan. 1977), 135-138. U.S. Department of Labor, Bureau of Labor Statistics, Youth and Wages, Bulletin 1657 (1970). Welch, Finis, Minimum Wage Legislation in the United States, Economic Inquiry 12 (Sept. 1974), 285-318. Minimum Wage Legislation in the United States: Reply, Economic Inquiry 15 (Jan. 1977), 139-142.

There appears to be an inconsistency Morton Paglin's recent article this Review. The effect of that inconsistency is to exaggerate the difference between the traditional Gini coefficient and a Gini coefficient which is adjusted for the average age-earnings relationship. Paglin observes that the Gini coefficient is often used normative evaluations of the income distribution. What begins as a measure of inequality is finally treated, with or without intervening statements of qualification, as if it were a measure of inequity. This being so we must ask whether all deviations from the mean income of the population-the inequalities that give magnitude to the Gini coefficent, G-are unjustifiable. For Paglin the answer is no, and on this point one suspects that he belongs, to a comfortable majority. The question is what to do. Paglin proposes that use be made of the average age-income relationship. Perfect equality or equity exists when all families at the same stage their life cycle have the same annual income. The deviation of any one family's income from the mean income for its age cohort is taken to be an unjustifiable deviation that gives magnitude to some adjusted Gini coefficient. Since the actual age-income profile has a definite hump, it is expected that the adjusted Gini coefficient will be less than G. My points will be that Paglin, giving mathematical expression to his normative position, has made a structural change the Gini formula which does not fully conform with the logic of the Gini measure, and that consequence the magnitude of inequality which he has measured is determinately too small. Suppose that the income scale and the age scale are partitioned into a finite number of segments. Each family belongs to one of the income ranges and to one of the age (of head) ranges so defined. Let nii represent the number of families that have an annual income income range i and whose family heads are age range j. They will be said to be in cell (i, j). The income level of every family cell (i, j) is denoted by yij.' The mean income level for all families age range j is denoted mj, and the grand mean is m. There are N families the population. The Gini coefficient,

This paper is concerned with the use of spectral analysis to analyze data generated by computer simulation experiments with models of economic systems. An example model serves to illustrate two different applications of spectral analysis. First, spectral analysis is used to construct confidence bands and to test hypotheses for the purpose of comparing the results of the use of two or more alternative economic policies. Second, spectral analysis is employed as a technique for validating an econometric model.