Educational Attainment and Mortality in the United States: Effects of Degrees,Years of Schooling,and Certification

Researchers have extensively documented a strong and consistent education gradient for mortality, with more highly educated individuals living longer than those with less education. This study contributes to our understanding of the education–mortality relationship by determining the effects of years of education and degree attainment on mortality, and by including non-degree certification, an important but understudied dimension of educational attainment. We use data from the mortality-linked restricted-use files of the Panel Study of Income Dynamics (PSID) sample (N = 9821) and Cox proportional hazards models to estimate mortality risk among U.S. adults. Results indicate that more advanced degrees and additional years of education are associated with reduced mortality risk in separate models, but when included simultaneously, only degrees remain influential. Among individuals who have earned a high school diploma only, additional years of schooling (beyond 12) and vocational school certification (or similar accreditation) are both independently associated with reduced risks of death. Degrees appear to be most important for increasing longevity; the findings also suggest that any educational experience can be beneficial. Future research in health and mortality should consider including educational measures beyond a single variable for educational attainment.     

Comparing mortality estimates based on different administrative records

This study investigates how age and race are reported in interviews and on death certificates and how these demographic assignments affect life expectancy estimates for the total population, for males and females, and for blacks and whites. Differential reporting of demographic characteristics can produce different estimated life expectancies and thus differential assessments of the sex and race gaps in life expectancy. This difference underscores the need to define demographic characteristics carefully and to examine multivariate models and projections cautiously.     

Assesssing Cohort Birth Expectations Data from the Current Population Survey, 1971–1981

Data from the fertility supplements to the Current Population Survey from 1971 to 1981 indicate that in the aggregate, the lifetime birth expectations of married women 18 to 39 years old in 1971 will closely approximate their completed cohort fertility. During this period, the youngest group of women, 18 to 24 years old, delayed their childbearing; their short-term expectations (1971–76) were not realized, but they made up enough births in the latter half of the decade to enable them to attain their lifetime birth expectations. In retrospect, the “failure” of birth expectations data to predict the “period” fertility downswing in the 1970s resulted not from poor predictions of married women, but rather from unanticipated marital and subsequent childbearing patterns of women who were single at the beginning of the decade. The authors conclude that birth expectations are useful predictors of completed cohort fertility, if adjustments are made to incorporate changes in the proportions married within the birth cohort.     

The multiregional matrix growth operator and the stable interregional age structure

Current population-forecasting efforts generally adopt minor variants of the cohort-survival projection method. This technique focuses on a population disaggregated into cohorts, a group of people having one or more common characteristics at a point in time and, by subjecting each cohort to class-specific rates of fertility, mortality, and net migration, generates a distribution of survivors and descendants of the original population, at successive intervals of time.Although cohort-survival methods take on a large number of variations, they all are essentially trend-based, dynamic, aspatial models of growth. The temporal element is introduced by a recursive structure which operates over a sequence of unit time intervals. The spatial dimension, when it is included at all, typically is accommodated by replicating the analysis over as many areal units as comprise the study area. Realistically, however, time and space need to be considered jointly in population-forecasting models. The need for interregional models which systematically introduce place-to-place movements and simultaneously consider the spatial as well as the temporal character of interrelated population processes is becoming increasingly apparent.Recently several demographers have taken advantage of the conceptual elegance and computational simplicity of matrix methods of population analysis. Their models, however, assume a "closed" population which is subject only to the processes of fertility and mortality. These, therefore, are not directly applicable to interregional "open" systems in which migration is frequently a much more variable and important contributor to population change than births or deaths. However, a natural extension of the demographer's matrix model allows one to incorporate place-to-place migration and provides an integrated interregional population-forecasting model which easily may be programmed for any of the current generation of digital computers. Such a model is outlined in this paper.     

Inventory Positioning/Partitioning for Backorders Optimization for a Class of Multi-Echelon Inventory Problems*

Inventory management has undergone significant philosophical changes in recent decades such as the advent of the zero inventory concept. However, as attractive as the concept of minimal inventories may be, it is often unrealistic in application. Attention to basic features of inventory control systems such as order quantities, base stock levels, and reorder points remain crucial to ensure customer service at minimal cost. A nonlinear optimization model for determining base stock levels in a multi-echelon inventory network is presented. Lagrangian relaxation results in (1) newsboy-style relations that provide the optimal solutions, and (2) instantaneous shadow prices for the budget constraint. Sensitivity analysis of this model will facilitate making decisions concerning the desired investment in inventory for the entire system. This model may be solved on standard nonlinear programming software and is generalizable to problems in both production and distribution settings.