Value of a Life: What Difference Does It Make?1

A comparison of the costs and benefits of 57 lifesaving programs reveals striking disparities across agencies and programs in cost/life saved and even greater disparities in cost/life-year saved. Within a broad range the monetary value assigned to the benefits of averting a death usually does not alter the policy implications of the analyses. The findings suggest that despite the substantial disagreements and uncertainties in the theory and practice of valuing lives, careful quantitative analysis can be helpful in setting health, safety, and environmental priorities.     

Business games and implementation research

This paper reviews some recent research in OR implementation and suggests that a new approach is required, which will take fuller account of the perceptions of managers concerned with the problem. Research games in business settings seem to offer a means for such approach. Some lessons learned from a business game used in this way are described and their implications for modelbuilders are discussed.     

Methods For Comparing The Mortality Experience of Heterogeneous Populations

Methods are presented which produce Maximum Likelihood Estimates (MLE) of the degree of heterogeneity in individual mortality risks under a variety of assumptions about the age trajectory of those mortality risks. With these estimates of the degree of population heterogeneity it is possible to adjust comparisons of mortality risks across populations for the effects of population heterogeneity, differential mortality selection, and different age trajectories of the force of mortality. These methods are demonstrated by applying a variety of standard assumptions about the age trajectory of the force of mortality to the analysis of a broad range of cohort mortality data for the U.S. and Swedish populations. The estimates of the degree of heterogeneity, produced under all of the selected force of mortality models, consistently indicated a considerable degree of heterogeneity in mortality risks.     

How Change in Age-specific Mortality Affects Life Expectancy

At current mortality rates, life expectancy is most responsive to change in mortality rates at older ages. Mathematical formulae that describe the linkage between change in age-specific mortality rates and change in life expectancy reveal why. These formulae also shed light on how past progress against mortality has been translated into increases in life expectancy – and on the impact that future progress may have. Furthermore, the mathematics can be adapted to study the effect of mortality change in heterogeneous populations in which those who did at some age would, if saved, enjoy a different life expectancy than those who live.     

Decomposing change in life expectancy: A bouquet of formulas in honor of Nathan Keyfitz’s 90th birthday

We extend Nathan Keyfitz's research on continuous change in life expectancy over time by presenting and proving a new formula for decomposing such change. The formula separates change in life expectancy over time into two terms. The first term captures the general effect of reduction in death rates at all ages, and the second term captures the effect of heterogeneity in the pace of improvement in mortality at different ages. We extend the formula to decompose change in life expectancy into age-specific and cause-specific components, and apply the methods to analyze changes in life expectancy in Sweden and Japan.     

Losses of Expected Lifetime in the United States and Other Developed Countries: Methods and Empirical Analyses

Patterns of diversity in age at death are examined using e ^†, a dispersion measure that equals the average expected lifetime lost at death. We apply two methods for decomposing differences in e ^†. The first method estimates the contributions of average levels of mortality and mortality age structures. The second (and newly developed) method returns components produced by differences between age- and cause-specific mortality rates. The United States is close to England and Wales in mean life expectancy but has higher life expectancy losses and lacks mortality compression. The difference is determined by mortality age structures, whereas the role of mortality levels is minor. This is related to excess mortality at ages under 65 from various causes in the United States. Regression on 17 country-series suggests that e ^† correlates with income inequality across countries but not across time. This result can be attributed to dissimilarity between the age- and cause-of-death structures of temporal mortality reduction and intercountry mortality variation. It also suggests that factors affecting overall mortality decrease differ from those responsible for excess lifetime losses in the United States compared with other countries. The latter can be related to weaknesses of health system and other factors resulting in premature death from heart diseases, amenable causes, accidents and violence.     

Experience with a multi-year fleet planning model

The fleet planning problem requires determination of the changing airline fleet mix based on existing traffic levels and projected growth, on the existing fleet, various aircraft types available in the future, and other operational and financial considerations. Several applicable mathematical formulations have been reported. One of these has been programmed for the computer and used by the authors for several years. This paper discusses the application of that model to real airline planning situations. Such practical experience has effected changes in the program and the manner it is used in fleet planning studies.     

A multi-dimensional model for projecting family households--with an illustrative numerical application

"This paper develops a multi-dimensional model for projecting households and population. The model is constructed to ensure consistency between the demographic events occurring to males and females as well as to parents and children. The model permits projection of characteristics of households, their members, and population structure, using data that are usually available from conventional sources. Unlike the traditional headship-rate method, our model can closely link the projected households with demographic rates. The model includes both nuclear and three-generation households, so that it can be used for countries where nuclear households are dominant and for countries where nuclear and three-generation households are both important. The illustrative application to China, although brief, provides some policy-relevant information about future trends of Chinese household size, structure, and the age and sex distribution of the population, with a focus on the elderly."