Count data models for demographic data

"This paper deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality....We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over- and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility." (SUMMARY IN FRE)     

Count data models for demographic data∗

Key demographic variables, such as the number of children and the number of marriages or divorces, can only take integer values. This papers deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality. Most empirical work in population economics has neglected the fact that the dependent variable is a nonnegative integer. In the few cases where this feature was recognized, the authors advocated the use of the Poisson regression model. The Poisson model imposes, however, the equality of conditional mean and variance, a restriction which is often rejected by the data. We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over‐ and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility.     

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.     

Degrouping mortality data for the elderly

Proposed in this paper is a technique for estimating, from coarsly grouped empirical death data, the age‐specific numbers of deaths for the elderly population. This question is primarily of interest in countries where the empirical data are available only in a grouped form, given usually in quinquennial age groups and in a large open‐ended interval for the ages 85 and over. The main reason that the official data are given in such a form in some countries of Southern Europe and in the Third World is the existence of heaping in the empirical data, i.e. misstatements in age recording, usually rounding to the nearest integer divisible with five. Our evaluation of the method on Swedish mortality data shows that the technique proposed can be efficiently applied to period mortality data.     

Estimating African-American mortality from inaccurate data

This paper evaluates the quality of vital statistics and census data for estimating African-American mortality over a period of six decades. The authors employ intercensal cohort comparisons and extinct generation estimates to demonstrate that conventionally constructed African-American death rates may be seriously flawed as early as age 50. Using the crude death rate at ages 50+ for 1978-1982 in conjunction with estimated growth rates and two model life table systems, the authors estimate black age-specific death rates in 1978–1982. These results suggest that if a racial crossover in death rates occurs, the age pattern of mortality among African-Americans must be far outside the range observed in populations with more accurate data.     

Guidelines for time use data collection

Growing and widespread interest in time begs the need for time use data collection and reporting standards. Initial guidance for comparative work was provided by the Multinational Time Use Project. However, changing technologies, methodologies, and divergent data needs have given rise to the need for updated guidance. This paper, prepared with input from members of the International Association for Time Use Research, examines the history and applications of time use data, identifies and evaluates methodological options for time use studies, recommends options facilitating cross-national and cross-temporal comparability and identifies methodological challenges facing time use researchers. The study concludes that alternative collection methods appear to make little difference in the resulting activity and time use estimates at customary levels of reporting.     

Understanding the 1996 census migration data

The Australian Census provides two approaches to measuring migration: indicators which distinguish movers from non-movers, and a geographic classification which identifies each person’s usual residence on census night, and their usual address one year and five years previously. Although these data represent a rich source of information, they contain several traps for the unwary. We show that differences in the variables and classifications used can result in marked variations in the apparent intensity and patterns of migration. The questionnaire and processing methodology used in the 1996 Census also resulted in a number of inconsistencies between the migration indicators and the usual address information. We examine the magnitude and source of these anomalies, assess their implications and propose a partial solution. The views expressed in this paper are personal and do not necessarily reflect those of the Australian Bureau of Statistics.     

Indigenous data sovereignty and COVID-19 data issues for American Indian and Alaska Native Tribes and populations

Journal of Population Research - Indigenous Peoples in the United States have been experiencing disproportionate impacts of COVID-19. American Indian and Alaska Native persons are more likely to be...     

Inferences about abortion from foetal mortality data

Abstract In a longitudinal fertility study in Detroit the evidence indicates that the foetal mortality rate in the prospective periods are better reported than in retrospective parts of the fertility histories. While the data do not specifically differentiate between induced abortions and other foetal deaths, the foetal death rates vary in relation to other social and demographic characteristics of the couples in such a way as make induced abortion a consistently plausible explanation of differentials. Foetal mortality rates tend to be high among sub-groups which have the incentive, information and the financial means for induced abortions. If these inferences are correct, it is likely that there is a considerable practice of induced abortion.     

Interpolation and forecasting of population census data

Since population censuses are not annually implemented, population estimates are needed for the intercensal period. This paper describes simultaneous implementations of the temporal interpolation and forecasting of the population census data, aggregated by age and period. Since age equals period minus cohort, age-period-cohort decomposition suffers from the identification problem. In order to overcome this problem, the Bayesian cohort (BC) model is applied. The efficacy of the BC model for temporal interpolation is examined in comparison with official Japanese population estimates. Empirical results suggest that the BC model is expected to work well in temporal interpolation. Regarding the age-period-cohort decomposition of the Japanese census data, it is shown that the cohort effect is the largest while the other two effects are very small but not negligible. With regard to the forecasting of the Japanese population, the official population forecast considerably outperforms the BC forecast in most forecast horizons. However, the pace of increase in root mean square error for longer-term forecasting is larger in the official population forecast than in the BC forecasts. As a result, a variant of the BC forecast is best for 10-year forecast.     

Consistent correction of data for aboriginal populations

A consistent correction procedure is used to determine improved, consistent estimates by sex of census age distributions, intercensal births, intercensal deaths and net migration by age for the Aboriginal populations of the Northern Territory, South Australia and Western Australia during the period 1986–91. Undercount estimates and life tables show the Aboriginal populations to have lower coverage in statistical collections and much higher death risks than the total Australian population. Inter-regional net migration estimates show that component of change can no longer be ignored.     

The quality of retrospective data on Cohabitation

We assess the quality of retrospective data on cohabitation by comparing data collected in four major U.S. family surveys: the National Survey of Families and Households and three rounds of the National Survey of Family Growth. We use event-history analysis to analyze rates of entry into cohabitation in age-period-cohort segments captured by multiple surveys. We find consistent discrepancies among the four surveys. The pattern of differences suggests that cohabitation histories underestimate cohabitation rates in distant periods relative to rates estimated closer to the date of survey. We conclude with cautions regarding the use of retrospective data on cohabitation.     

Forecasting German mortality using panel data procedures

Reliable forecasts of life expectancies are of importance for the financial stability of social security systems and the life insurance industry. A discrete-time stochastic process and a continuous-time stochastic process are proposed to model the dynamics of German mortality rates from which life expectancies are calculated. More precisely, a panel data model is utilized, which distinguishes between a common time effect and a common age effect. The model is easy to fit, yields interpretable parameters, and allows for a simple analysis of the forecast error. The main applications of the model are the forecast of mortality rates—and the resulting life expectancies—and the pricing of mortality derivatives.

The verification of data in historical demography

Abstract In all scientific studies care must be taken to work on data which are correct and hence to make sure of the quality of the observations. In the field of historical demography there is need for extra care. The data were collected a long time ago in circumstances which were sometimes good, sometimes bad, but often little known. Statisticians are, moreover, on a priori grounds, rather inclined to be more suspicious of observations made in the past than of those which are made currently. Results relating to past populations must therefore be more rigorously established than any others if they are to be accepted. The verification of the data thus forms an integral part of the work of the historical demographer.     

Fertility estimation from open birth-interval data*

Censuses and surveys frequently collect information on period fertility through questions on the timing of last births. The standard approach to estimating fertility with open-interval data uses the proportion of women giving birth in the year before the interview. I propose a more efficient, maximum likelihood method for estimating fertility from open-interval data. I illustrate a mathematical derivation of the new method, perform sensitivity analyses, and conduct empirical tests with Brazilian census data. The new estimators have small biases and lower variance than standard estimators for open-interval data. Consequently, the new method is more likely to generate accurate results from small or moderately sized samples.     

Annual fertility rates from census data on own children: Comparisons with vital statistics data for the United States

This paper begins by describing the procedure and data requirements for calculating annual fertility rates from census data on own children. Then, using data from the United States Censuses of 1960 and 1970, fully adjusted estimates are presented and compared with recorded vital statistics rates. Total fertility estimates derived from own children data for whites average less than two percent lower than the recorded rates- a difference that can be attributed partially to the fact that the estimates are adjusted for net census undercount but the recorded rates are not. Even without adjustments for mortality, children not living with their mothers, and net census undercount, the own children data estimates accurately replicate recorded trends (even though the levels are misspecified). The utility of own children data for the study of differential fertility is discussed.