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We construct a new measure of the worldwide variation in human welfare as defined by life-sustenance, esteem, and freedom. A nation's physical, economic, social, and political systems should provide resources, institutions, and cultural environments that foster human welfare. The new Index of Development Status i s an improvement on previous indices because it is built on concepts of human welfare and produces a robust, versatile measure that provides multidimensional insights about world variations.In constructing the new Index, we selected the following variables of systems outputs: physical-total value of primary industry output per capita, persons per square kilometer of arable land; economic-CNPIcapita, and manufacturing value-added per capita; social-infant deaths per 1000 live births, percentage of age group in higher education, and percentage of rural population; politicalgovernment expenditures per capita, political rights index, and number of radios per 1000 population. Factor analysis with varimax rotation yielded four principal factors that correspond conceptually with the physical, economic, social, and political systems. Based on factor scores, we arrayed 160 countries according to the sum of the quintile ranks of their four systems, from most developed to least developed. Various combinations of quintile scores permit us to rank and map countries for overall human welfare, socioeconomic human welfare, sociopolitical human welfare, and politico-economic human welfare. The scale of analysis can be varied to sub-national regions, variables can be added t o each macrosystem to extend the concepts

There are a variety of approaches to the problem of forecasting educational enroll ment. This paper outlines a structural model for projecting public school enroll ment by grade, program, and district. The advantage of specifying a structural model is that many of the standard approaches to forecasting enrollment emerge as spe cial cases of its reduced form equations. The flexibility of this approach also ena bles a model to be selected that is con sistent with the goals of the forecaster and the availability of data. These points are illustrated by an application to the prob lem of forecasting enrollment in Florida. General ConsiderationsPublic school enrollment is largely de mand determined. That is, the public schools are legally required to supply ed ucational services to whatever number of students present themselves at their doors. The state is willing to fund any reasonably sized program in any district, and to pro vide transportation to other districts in many cases. What varies is the quality of the education provided and its cost. With a given number of students in any grade, the school has some limited discretion over the allocation of these students across pro grams. This makes the projection of en rollment by program in any grade more complex than the projection of total en rollment in the grade. For this reason we begin by projecting total enrollment by grade and then apportion downward by program.A forecaster has a choice of three spatial units on which to base his projections: the school, the school district, or some aggre gate of the districts such as the state. A reasonable compromise between the ex cessive sensitivity of enrollment by school to local factors, and the insensitivity of enrollment by state or aggregate of a num ber of districts to such factors, is to base the forecast on total enrollment by school district.Given a projection of total enrollment by grade by district, the students must be distributed across the relevant programs in each grade. These programs can be grouped into three broad categories: basic education, vocational education, and ex ceptional or special education. The second part of the projection model outlined here contains a procedure for allocating total enrollment by grade into these three pro gram areas. The last part of the model provides an allocation of vocational and exceptional enrollment into the individual programs within each of these general areas.

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Two measures of female status displayed dissimilar spatial distributions. Absolute status, measured by income, education, and occupation, was highest in the West and Northeast and lowest in the South. Female status was high where male status was high. The relative status of women compared to men, however, was high in the West and the rural Middle West and South but low in the Mountain West and industrial Northeast. Nationwide, where men enjoyed high absolute status, the relative status of women compared with men was poor.