The Measure of Population Aging in Different Welfare Regimes: A Bayesian Dynamic Modeling Approach.

Given a population at a specific time point, it is often of interest to identify the entry age into typical stages of life, such as being young, becoming adult and elderly. These age cutoffs are important because they influence the public opinion and have an impact on policy decisions. An issue of great social relevance is defining the threshold beyond which a person becomes elderly. Fixed cutoffs are debatable because of their conventional nature which disregards issues such as changing life expectancy and the evolving structure of the age distribution. The above shortcomings can be overcome if age cutoffs are defined endogenously, i.e., relative to the whole age distribution of each country at a specific time point. We pursue this line of research by presenting an analysis whose main features are: (1) establishing a relationship between a country's welfare regime and its age distribution and aging process, together with the identification of four clusters of countries corresponding to distinctive welfare models and (2) a Bayesian hierarchical dynamic model which accounts for the uncertainty in the time series of measurements of the endogenous cutoffs for the countries in the sample, as well as for their clustering structure. Our analysis leads to model-based estimates of country-specific endogenous age cutoffs and corresponding aging indicators. Additionally, we provide cluster-specific estimates, a novel contribution engendered by the use of hierarchical modeling, which widens the scope of our analysis beyond the countries which are present in the sample.

Objective Bayesian Comparison of Constrained Analysis of Variance Models.

In the social sciences we are often interested in comparing models specified by parametric equality or inequality constraints. For instance, when examining three group means [Formula: see text] through an analysis of variance (ANOVA), a model may specify that [Formula: see text], while another one may state that [Formula: see text], and finally a third model may instead suggest that all means are unrestricted. This is a challenging problem, because it involves a combination of nonnested models, as well as nested models having the same dimension. We adopt an objective Bayesian approach, requiring no prior specification from the user, and derive the posterior probability of each model under consideration. Our method is based on the intrinsic prior methodology, suitably modified to accommodate equality and inequality constraints. Focussing on normal ANOVA models, a comparative assessment is carried out through simulation studies. We also present an application to real data collected in a psychological experiment.