This book chapter starts by discussing the major producers of international, national, and subnational projections. Next, it provide a description of the methods and materials used in preparing three basic types of population projections: (1) trend extrapolation; (2) the cohort-component method; and (3) structural modeling. The authors briefly discuss methods for preparing related projections on such topics as school enrollment, employment, and households.
Developments in economic theory over the last 20 years have placed decisions regarding female labor force participation (FLFP) and fertility within a model of household decision making (HDM). In this one period, static model utility is a function of child services (including both number and quality of children), market goods and services, and leisure. At the outset of their marriage a husband and wife adopt a utility-maximizing lifetime plan of fertility, market work, nonmarket activities, and consumption of goods and services, subject to income and time constraints.
Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States.
A number of studies in recent years have investigated elnpirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of tilne periods during the 20th century, comparing these projections with census enumerations to deter~nine forecast errors, and analyzing the stability of the resulting error distributions over time.
This article develops three different models of migration for cohort-component projections, each using a different base (i.e., denominator) for migration rates. The differences in the resulting projections are analyzed, and a number of conclusions are drawn regarding the construction of migration rates for use in cohort-component population projections.
The housing unit method of population estimation is often characterized as being imprecise and having upward bias. In an earlier paper we argued that the method itself cannot be properly characterized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In that paper we presented several new techniques for estimating the number of households and average number of persons per household (PPH).
The housing unit method of population estimation is often characterized as being imprecise and having upward bias. We believe that the method itself cannot properly be categorized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In this paper we discuss several new techniques we have developed for estimating households and the average number of persons per household.
Many business, political, and personal disputes in the United States are settled only after passing through the nation's judicial or regulatory system. The culmination of this process is frequently a hearing or triaI in which the opposing parties argue the merits of the case. Demographic factors play a critical role in many of these disputes and demographers are often called upon to testify in hearings or trials. This article discusses the role of the demographer as expert witness and offers some tips on how to prepare and present expert testimony.
In the housing unit method, population is calculated as the number of households times the average number of persons per household (PPH), plus the population residing in group quarters facilities. Estimates of households and the group quarters population can be derived directly from concurrent data series, but estimates of PPH have traditionally been based on previous values or estimates for larger areas. In our study, we developed several regression models in which PPH estimates were based on symptomatic indicators of PPH change.
A number of studies have evaluated the accuracy ofprojections of the size of the total population, but few have considered the accuracy of projections by age group. For many purposes, however, the relevant variable is the population of a particular age group, rather than the population as a whole. We investigated the precision and bias of a variety of age-group projections at the national and state levels in the United States and for counties in Florida.