Modeling social and demographic phenomena: Mortality, inequality and labor force growth

This dissertation presents three papers on social and demographic modeling. The aim of social and demographic modeling is to accurately assess a dimension, trend, or relationship in a population using mathematical formulae. The first paper is an analysis of a popular method of modeling mortality distributions. Brass hypothesized that there is a linear relationship between the logits of the cumulative probability of dying before age x in a standard mortality distribution and those observed in any population. In this paper I analyze and apply five statistical methods used to estimate Brass\u27s linear parameters. Of all the methods discussed, the Maximum Likelihood Estimation procedure produced the most consistent mortality estimates with the least assumptions. The second paper is on measuring relative deprivation. In this paper I present a popular measure of relative deprivation and advance an alternative index based on an existing poverty measure. Using these measures, and an inequality measure, I conclude that the alternative relative deprivation index indicates a different trend in well-being in relation to the inequality and popular relative deprivation measures. The last paper is an analysis of labor force growth in the United States since 1950. Using demographic methodology, I decompose labor force growth into demographic and social components. Since 1950 demographic processes played the largest role in labor force growth. The social factor that made the largest contribution to labor force growth since 1950 was the changing participation rates of women; specifically married women

Modeling social and demographic phenomena: Mortality, inequality and labor force growth

This dissertation presents three papers on social and demographic modeling. The aim of social and demographic modeling is to accurately assess a dimension, trend, or relationship in a population using mathematical formulae. The first paper is an analysis of a popular method of modeling mortality distributions. Brass hypothesized that there is a linear relationship between the logits of the cumulative probability of dying before age x in a standard mortality distribution and those observed in any population. In this paper I analyze and apply five statistical methods used to estimate Brass\u27s linear parameters. Of all the methods discussed, the Maximum Likelihood Estimation procedure produced the most consistent mortality estimates with the least assumptions. The second paper is on measuring relative deprivation. In this paper I present a popular measure of relative deprivation and advance an alternative index based on an existing poverty measure. Using these measures, and an inequality measure, I conclude that the alternative relative deprivation index indicates a different trend in well-being in relation to the inequality and popular relative deprivation measures. The last paper is an analysis of labor force growth in the United States since 1950. Using demographic methodology, I decompose labor force growth into demographic and social components. Since 1950 demographic processes played the largest role in labor force growth. The social factor that made the largest contribution to labor force growth since 1950 was the changing participation rates of women; specifically married women

BRASS' RELATIONAL MODEL: A STATISTICAL ANALYSIS

Brass' relational model is based on a linear relationship between the logits of the cumulative probability of dying before age x in a standard mortality distribution and those observed in any population. In this study the appropriate way to estimate the linear parameters associated with Brass' model is clarified. Five methods are presented to estimate the coefficients associated with Brass' relational model. Each method is applied to simulated data to examine the efficiencies of each model in mortality estimation.