On the estimation of multidimensional demographic models with population registration data

In this paper the estimation of multidimensional demographic models is investigated in situations where population registration data are available. With this kind of aggregate data, estimation by traditional methods is not possible. We look at two versions of the multidimensional model: the constant intensities model and the linear integration model. Some logical inconsistencies in the derivation of the latter are discussed. In particular, we argue that the linear integration model is not compatible with a Markov process. A new algorithm for the estimation of the constant intensities model with population registration data is proposed. Some preliminary results on the mathematical and statistical properties of this method are given. The algorithm is applied to Dutch nuptiality data.     

The development and use of demographic models

In this review, we first examine two classical demographic models - conventional life tables and stable populations - and a modern generalization of stable population theory; we then discuss mathematical models of conception and birth. These models involve purely mathematical relations in formal demography as opposed to empirical regularities. Next we consider model age schedules of mortality, nuptialitiy, marital fertility, fertility, and migration that are explicitly based on such empirical patterns. We close this empirical section with a discussion of model stable populations, which are based on model life tables. We next examine the use of demographic models in forecasting future mortality, nuptiality, and fertility and in population projection. Following a discussion of microsimulation models, which gives us the opportunity to mention model age schedules of post partum amenorrhoea and of sterility, we close with observations about the purposes and uses of demographic models.     

Investigating demographic processes using innovative combinations of remotely sensed and demographic data

Population and Environment -     

Correction to: Investigating demographic processesusing innovative combinations of remotelysensed and demographic data

Population and Environment - The original version of this article unfortunately contained a mistake. The name “Kluger” in the second paragraph should be changed to “Kugler”.     

Biosocial models of demographic behavior: An introduction

A workshop on biosocial models of demographic behavior was organized to provide information to members of the Social Sciences and Population Study Section (SSP), the group entrusted by the National Institutes of Health (NIH) with the responsibility for conducting the first level of peer review of demographic applications submitted to NIH for possible funding. Some of the variables studies by demographers are biological, e.g., fertility, fecundity, morbidity, and mortality, so demographers are not unaware of biological variables. However, they tend to treat biological variables as something to be explained by social, economic, and psychological factors rather than to be integrated into an explanatory paradigm. This workshop contains papers that focus upon various stages of the life cycle and explore the importance of biosocial variables in explaining selected aspects of human behavior.This introduction presents an overview of the topics covered by the authors of the papers presented and the workshop, and is based upon opening remarks at the DRG Workshop on Biosocial Models of Demographic Behavior, Bethesda, MD, 12 October 1994.     

Optimization models for degrouping population data

In certain countries population data are available in grouped form only, usually as quinquennial age groups plus a large open-ended range for the elderly. However, official statistics call for data by individual age since many statistical operations, such as the calculation of demographic indicators, require the use of ungrouped population data. In this paper a number of mathematical models are proposed which, starting from population data given in age groups, enable these ranges to be degrouped into age-specific population values without leaving a fractional part. Unlike other existing procedures for disaggregating demographic data, ours makes it possible to process several years' data simultaneously in a coherent way, and provides accurate results longitudinally as well as transversally. This procedure is also shown to be helpful in dealing with degrouped population data affected by noise, such as those affected by the age-heaping phenomenon.     

Assessing state population projections with transparent multiregional demographic models

Population projection models that are conceptually simple enough to be called transparent may be used to check the validity of projections generated by black box models whose behavior may be somewhat shrouded in mystery. This paper adopts a multiregional demographic model to illustrate how such a validation procedure might be carried out on state population projections produced by the U.S. Bureau of the Census in 1988.     

Software for demographic research

Software that has been specifically developed to perform demographic analyses on both microcomputers and mainframes is reviewed. The emphasis is on those programs with wide distribution. Consideration is given to packages of demographic programs; fertility estimation and analysis; mortality estimation and analysis; nuptiality; migration; data collection, management, and quality testing; and population projections and models. Information is included on the addresses of the distributors of the software packages and programs.     

A comparison of three maximum likelihood models for stage-frequency data

Summary A comparison has been made between the estimates obtained from maximum likelihood estimation of gamma, inverse normal, and normal distribution models for stage-frequency data. Results have been compared for six of sets of test data, and from many sets of simulated data. It is concluded that (1) some estimates may differ substantially between the models, (2) estimates from the correct model have little bias, and estimated standard errors are generally close to theoretical values, (3) there are problems in determining degrees of freedom for chi-squared goodness of fit tests, so that it is best to compare test statistics with simulated distributions, and (4) goodness of fit tests may not discriminate well between the three models.     

Malthusian models and Chinese realities: the Chinese demographic system, 1700-2000

This article summarizes major recent findings on Chinese demographic behavior and outlines their relevancy for the Malthusian model of comparative population dynamics and Chinese population in particular. Specifically, it considers four distinctive and persistent features of Chinese behavior during the last 300 years—high rates of female infanticide and abortion, high rates of bachelorhood, low marital fertility, and high rates of male and female adoption–and discusses the origins and implications of such a demographic regime for Chinese economic and social development. Contrasting Chinese demographic behavior with European demographic behavior, the article argues the existence of a demographic system and a demographic transition different from current Malthusian and neo-Malthusian models, and the existence of a system regulating collective demographic behavior in ways distinctly different from Western experience.     

Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models

This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.     

Population momentum for gradual demographic transitions

Population momentum is the ratio of a population's ultimate size after a demographic transition to its initial size before the transition. For an instantaneous drop to replacement fertility, Nathan Keyfitz found a simple expression M K for the momentum. However, as Keyfitz pointed out, `no one thinks that any country will drop immediately to stationary reproduction patterns'. We present results concerning the momentum of a population whose demographic transition is completed within a finite time. First, we provide an exact analytical formula for such a population's momentum. Second, for rapid fertility transitions, we obtain a simple exact expression for momentum that reduces to Keyfitz's M K if the transition is instantaneous. We show, by example, that our simpler formulae are accurate approximations to population momentum for transitions that take as long as 100 years. Finally, we show that the speed of fertility decline makes a substantial difference to population momentum.     

The perils of father-reported fertility data for household labour supply models

Many panel data-sets contain father-reported fertility data. Yet, since men tend to underreport past fertility--especially daughters or children from previous marriages--using such data can lead to significantly biased results when estimating household labour supply models. For example, when analysing fertility data from the Panel Study of Income Dynamics, which has a significant retrospective component, fathers' labour supply appears more responsive to the births of sons than to daughters (Lundberg and Rose, Review of Economics and Statistics 84(2): 251-268, 2002). However, no evidence for this differential can be found in a much larger sample of Current Population Survey data from the same population. It is important for researchers to consider the provenance of data on fertility variables and to undertake robustness checks with data reported by women whenever possible.     

Methods for combining ancillary data in stochastic compartment models of cancer mortality: generalization of heterogeneity models

"We present a mortality model where nationally representative survey data on risk factor distributions are combined with data on cohort mortality rates to increase information, i.e., a fixed marginal risk factor distribution is combined with a cohort model representing unobserved individual risk heterogeneity. The model is applied to lung cancer mortality in nine U.S. white male cohorts aged 30 to 70 in 1950 and followed 38 years. Estimates of the cohort specific proportions of smokers were made from the National Health Interview Survey. Comparisons are made for models with different patterns of changes with age of individual heterogeneity." (SUMMARY IN FRE)     

Modeling female fertility using inflated count data models

For modeling complete female fertility we propose a zero-and-two-inflated count data model, which accounts for a relative excess of both zero and two children. As the underlying distribution of counts we use the standard Poisson distribution and the more general Gamma count distribution. We compare our proposed model with standard count data models by using data on complete fertilities for a sample of Swedish women. The preferred specification for Swedish fertility data is the zero-and-two inflated Gamma count data model. The estimated “extra” probabilities of zero and two children, when modelled as individual specific probabilities, vary substantially across individuals, with mean of 0.05 and 0.16, respectively. These extra probabilities show that women who formed a family later in life have a higher probability of being childless, and women of our youngest cohort have a higher probability of forming a two-child family. Received: 7 January 1999/Accepted: 19 May 1999