Applied Mathematical Demography

What follows is a new edition of the second in a series of three books providing an account of the mathematical development of demography. The first, Introduction to the Mathematics of Population (Addison-Wesley, 1968), gave the mathematical background. The second, the original of the present volume, was concerned with demography itself. The third in the sequence, Mathematics Through Problems (with John Beekman; Springer Verlag, 1982), supplemented the first two with an ordered sequence of problems and answers. Readers interested in the mathematics may consult the earlier book, republished with revisions by Addison-Wesley in 1977 and still in print. There is no overlap in subject matter between Applied Mathematical Demography and the Introduction to the Mathematics of Population. Three new chapters have been added, dealing with matters that have come recently into the demographic limelight: multi-state calculations, family demogra phy, and heterogeneity. vii PREFACE This book is concerned with commonsense questions about, for instance, the effect of a lowered death rate on the proportion of old people or the effect of abortions on the birth rate. The answers that it reaches are not always commonsense, and we will meet instances in which intuition has to be adjusted to accord with what the mathematics shows to be the case.

Mathematical demography is the centerpiece of quantitative social science. The founding works of this field from Roman times to the late Twentieth Century are collected here, in a new edition of a classic work by David R. Smith and Nathan Keyfitz. Commentaries by Smith and Keyfitz have been brought up to date and extended by Kenneth Wachter and Hervé Le Bras, giving a synoptic picture of the leading achievements in formal population studies. Like the original collection, this new edition constitutes an indispensable source for students and scientists alike, and illustrates the deep roots and continuing vitality of mathematical demography.

This book gives a unified presentation of, and mathematical framework for, modeling population growth by couple formation, summarizing both past and present modeling results. It provides results on model analysis, gives an up-to-date review of mathematical demography, discusses numerical methods, and puts deterministic modeling of human populations into historical perspective.

Mathematical theories of populations have appeared both implicitly and explicitly in many important studies of populations, human populations as well as populations of animals, cells and viruses. They provide a systematic way for studying a population's underlying structure. A basic model in population age structure is studied and then applied, extended and modified, to several population phenomena such as stable age distributions, self-limiting effects, and two-sex populations. Population genetics are studied with special attention to derivation and analysis of a model for a one-locus, two-allele trait in a large randomly mating population. The dynamics of contagious phenomena in a population are studied in the context of epidemic diseases.

Mathematical Demography, the study of population and its analysis through mathematical models, has received increased interest in the mathematical com munity in recent years. It was not until the twentieth century, however, that the study of population, predominantly human population, achieved its math ematical character. The subject of mathematical demography can be viewed from either a deterministic viewpoint or from a stochastic viewpoint. For the sake of brevity, stochastic models are not included in this work. It is, therefore, my intention to consider only established deterministic models in this discussion, starting with the life table as the earliest model, to a generalized matrix model which is developed in this treatise. These deterministic models provide sufficient de velopment and conclusions to formulate sound mathematical population analy sis and estimates of population projections. It should be noted that although the subject of mathematical demography focuses on human populations, the development and results may be applied to any population as long as the preconditions that make the model valid are maintained. Information concerning mathematical demography is at best fragmented.

Multidimensional Mathematical Demography is a collection of papers dealing with the problems of inaccurate or unavailable demographic data, transformation of data into probabilities, multidimensional population dynamic models, and the problems of heterogeneity. The papers suggest a unified perspective with emphasis on data structure to work out multidimensional analysis with incomplete data. To solve inaccuracies in data, one paper notes that designs and use of model multistate schedules, for example, methods of inferring data, should be a major part in multistate modeling. Other papers discuss the state-of-the-art in abridged increment-decrement life table methodology. They also describe the estimation of transition probabilities in increment-decrement life tables where mobility data available is from the count of movers from a population survey. One paper reviews the possible extension of a multiregional stochastic theorem associated in a single-regional case; and then analyzes what the stochastic model needs when it is used with real data. Another paper explains strategies concerning population heterogeneity when it pertains to the mixtures of Markov and semi-Markov processes; Markov processes subject to measurement error; and the Heckman and Borjas model. This collection can be read profitably by statisticians, mathematicians, mathematical demographers, mathematical sociologists, economists, professionals in census bureaus, and students of sociology or geography.

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.