A First Exposure to Statistical Mechanics for Life Scientists

Statistical mechanics is one of the most powerful and elegant tools in the quantitative sciences. One key virtue of statistical mechanics is that it is designed to examine large systems with many interacting degrees of freedom, providing a clue that it might have some bearing on the analysis of the molecules of living matter. As a result of data on biological systems becoming increasingly quantitative, there is a concomitant demand that the models set forth to describe biological systems be themselves quantitative. We describe how statistical mechanics is part of the quantitative toolkit that is needed to respond to such data. The power of statistical mechanics is not limited to traditional physical and chemical problems and there are a host of interesting ways in which these ideas can be applied in biology. This article reports on our efforts to teach statistical mechanics to life science students and provides a framework for others interested in bringing these tools to a nontraditional audience in the life sciences.Comment: 27 pages, 16 figures. Submitted to American Journal of Physic

Age Variations in Workers' Value of Statistical Life

This paper develops a life-cycle model in which workers choose both consumption levels and job fatality risks, implying that the effect of age on the value of life is ambiguous. The empirical analysis of this relationship uses novel, age-dependent fatal and nonfatal risk variables. Workers' value of statistical life exhibits an inverted U-shaped relationship over workers' life cycle based on hedonic wage model estimates, age-specific hedonic wage estimates, and a minimum distance estimator. The value of statistical life for a 60-year old ranges from $2.5 million to$3.0 million -- less than half the value for 30 to 40-year olds.

FINITE LIFE EXPECTANCY AND THE AGE-DEPENDENT VALUE OF A STATISTICAL LIFE

In this short paper, we investigate the behavior of the age-dependent value of a statistical life (VSL) within a lifecycle framework with a finite maximal possible lifespan. Some existing results, obtained under the unrealistic assumption of an infinite life expectancy, are reversed. In particular, we show that when the market interest rate is equal to (or less than) the sum of age-specific mortality rate and the discounting rate in time preference at any age over the remaining lifetime, then VSL declines. We also show that an inverted-U shape of VSL profile over the life cycle emerges under realistically plausible circumstances. An innovation is that we characterize the changes in optimal consumption and instantaneous utility with age, showing that such changes are proportionate to the difference between the sum of age-specific mortality rate and the discounting rate in time preference and the market interest rate, which may prove to be useful in addressing other issues related to VSL.Value of life; life expectancy; interest rates; time preference; mortality.

A statistical distribution useful in product life-cycle modeling

Starting from some recent results presented by Isaic â Maniu and Vodă (2008) regarding the Product Life â Cycle (PLC), we propose here an alternative statistical distribution in order to describe all four phases of a product life span. This distribution is the so-called ALPHA distribution which was formerly used in reliability theory as time-to-failure distribution.PLC (Product Life-Cycle), life span, statistical modeling, Alpha distribution.

The Rights of Statistical People

In this Comment, I argue that the use of cost-benefit analysis to evaluate life-saving regulatory programs has, in a society that eschews reliance on cost-benefit analysis in other life-saving situations, been justified by the creation of a new kind of entity-the statistical person. A primary feature of the statistical person, as I will explain, is that she is unidentified; she is no one\u27s sister, or daughter, or mother. Indeed, in one conception, the statistical person is not a person at all, but rather only a collection of risks. By distinguishing statistical lives from the lives of those we know, economic analysts have attempted to sidestep the uncomfortable fact that most of us profess ourselves quite incapable of identifying the monetary equivalent of the lives of our sisters, daughters, mothers, and friends

Labor Market Estimates of the Senior Discount for the Value of Statistical Life

This article develops the first measures of age-industry job risks to examine the age variations in the value of statistical life. Because of the greater risk vulnerability of older workers, they face flatter wage-risk gradients than younger workers, which we show to be the case empirically. Accounting for this heterogeneity in hedonic market equilibria leads to estimates of the value of statistical life-age relationship that follows an inverted-U shape. The estimates of the value of statistical life range from $6.4 million for younger workers to a peak of$9.0 million for those age 35-44, and then a decline to $3.7 million for those age 55-62. The decline of the estimated VSL with age is consistent with there being some senior discount in the Clear Skies Initiative analysis.value of statistical life, job risks, senior discount, compensating differentials