With special reference to India.

Utilizing the most recent developments in statistical modeling as applied to population studies, the authors interpret results obtained from available software and apply these results to current research issues.

This book is intended as a relatively nontechnica1 introduction to eurrent demographie methods. It has been several years in preparation, beginning from occasional class handouts I wrote to elaborate on essential points of demographie methodology. Its growth from scattered notes to an integrated text was a natural process, if a gradual one. The eontent of the book addresses three objectives. first, I have tried to avoid demographie methods that are now dated. In some ehapters, that has meant eoncentrating on formulas most demographers recognize. In the ehap ters on life tables, it meant testing competing formulas on a variety of real and synthetie data se.ts, and dropping or relegating to footnotes those that were least accurate. Second, I have attempted to give readers a sense of the limits of different formulas and methods. I am a terse writer, however, and for the reader that means most sentences carry weight. Chapters should be read attentively, with careful regard to commentary as weIl as to formulas and examples. Finally, I have tried to make the principal methodologies of the book accessible, by offering explanations for formulas that are not obvious, by keeping examples to the forefront, and by placing relatively specialized topics in ehapter appendices.

In the 50 years that have passed since Alfred Latka's death in 1949 his position as the father of mathematical demography has been secure. With his first demographic papers in 1907 and 1911 (the latter co authored with F. R. Sharpe) he laid the foundations for stable population theory, and over the next decades both largely completed it and found convenient mathematical approximations that gave it practical applica tions. Since his time, the field has moved in several directions he did not foresee, but in the main it is still his. Despite Latka's stature, however, the reader still needs to hunt through the old journals to locate his principal works. As yet no exten sive collections of his papers are in print, and for his part he never as sembled his contributions into a single volume in English. He did so in French, in the two part Theorie Analytique des Associations Biologiques (1934, 1939). Drawing on his Elements of Physical Biology (1925) and most of his mathematical papers, Latka offered French readers insights into his biological thought and a concise and mathematically accessible summary of what he called recent contributions in demographic analy sis. We would be accurate in also calling it Latka's contributions in demographic analysis.

The initial plans for this book sprang from a late-afternoon conversation in a hotel bar. All three authors were attending the 1996 meeting of the Population As- ciation of America in New Orleans. While nursing drinks and expounding on a variety of topics, we began talking about our current research projects. It so happened that all three of us had been entertaining the notion of writing a book on state and local population projections. Recognizing the enormity of the project for a single author, we quickly decided to collaborate. Had we not decided to work together, it is unlikely that this book ever would have been written. The last comprehensive treatment of state and local population projections was Don Pittenger’s excellent work Projecting State and Local Populations (1976). Many changes affecting the production of population projections have occurred since that time. Technological changes have led to vast increases in computing power, new data sources, the development of GIS, and the creation of the Internet. The procedures for applying a number of projection methods have changed considerably, and several completely new methods have been developed.

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.