Sideline demographic methods and data

This presentation shall introduce the Survey of Health, Ageing and Retirement in Europe (SHARE) and its potentials for multidisciplinary comparative research on ageing and the life course. SHARE is coordinated at the Munich Center for the Economics of Aging (MEA), Max-Planck Institute for Social Law and Social Policy. It is a multidisciplinary cross-national panel survey designed to inform evidence based policies. The first two waves – collected in 2004-05 and 2006-07, respectively – provide comprehensive data on the current economic, health, and social and family networks of more than 45,000 individuals aged 50 and over in 14 European countries (including Israel). The project’s third wave – SHARELIFE – has collected retrospective information on the life-histories including family, work and employment histories of these individuals in 2008-09. The fourth panel wave collected in 2010-11 includes 80,000 respondents from 19 European countries and added comprehensive information on social networks. SHARE and its sister surveys thus provide information on all key aspects of individual and population ageing in the “natural laboratory” of different cultures, policies and histories worldwide. We will present multidisciplinary findings on health, socio-economics and social networks across the life course to demonstrate the value of SHARE

Existing cross-sectional analyses on grandparental involvement in parenting face the problem that they are unable to disentangle selection and adaptation processes. It may well be that intergenerational relationships may have been differed already between individuals with and without a transition to parenthood before the event of the birth of a child (selection effect). But it may also well be that the birth of a child may change the intergenerational relationships because of changed demands and opportunities (adaptation effect). Adaptation may operate in opposing directions: In case, the demand for intergenerational solidarity meets supply from the grandparental side, this may enhance the existing intergenerational relationship; in case of incongruency, the event may result in increased intergenerational conflict or detachment.
The German Family Panel (pairfam) is chosen as the appropriate data-set to empirically answer several of the related research questions. The chosen example of changes in intergenerational relationships will demonstrate the necessity of data-sets, which combine a multi-actor with a panel-design, as an important tool in the realm of demographic and family research. This design of pairfam allows for disentangling selection- and adaptation effects.

The German National Educational Panel Study provides high-quality data about educational processes and competence development from early childhood to late adulthood. Based on a multicohort sequence design six representative samples covering the whole life-span (newborns, children in Kindergartens, 5th graders, 9th graders, college freshmen, and adults) with a total amount of about 60,000 target persons were drawn and followed-up. The data collected in all six groups focus on (1) competence development, (2) learning environments, (3) educational decisions and social inequality, (4) effects of migration background, and (5) returns to education. The presentation focuses on data of the NEPS adult survey, which consists of more than 11,500 participants born between 1944 and 1986. Due to its strong focus on collecting retrospective life-course data with state-of-the-art computer assisted interviews, it is particularly suited for analyzing demographical questions. Exemplary analyses – dividing the total sample in broad cohorts – demonstrate the analytical potential of the data set. They illustrate the relation between different structural conditions, individual characteristics (socio-demographic background), individual decisions (e.g., educational decisions and labor marketparticipation), generative decisions and patterns of aging.

The aim of this contribution is to highlight the potential of the German Socio-Economic Panel Study in conducting demographic analyses, and to provide evidence from two selected empirical studies. Starting in 1984, the SOEP provides long running panel data, representative of the German population. Repeated observations among the same individuals, combined with biographical and retrospective survey modules span an arc of information from conception to death. The SOEP covers key topics on fertility, migration, and mortality, including information on marital and fertility histories, migration background and a register-based follow-up study of attritors to provide a full coverage of emigration and deaths. Importantly, the SOEP enables researches to related these demographic indicators to a broad variety of background factors, including education, income, activity status, health, attitudes, and psychological traits, to name only a few. The SOEP offers a high level of comparability to other national panel studies, including the BHPS, PSID, HILDA. This renders the SOEP a key resource for cross-national research. Selected findings include a cross national study on the impact of economic uncertainty on fertility decisions in Germany, and the UK, as well as a study on the impact of economic uncertainty on fertility decisions in Germany.

Structural Equations Models (SEM) are normally used to test the agreement between the data and a hypothesized model of causal relationships among multiple variables. However, in many circumstances it is hard to define one or several causal models to test, either because there is insufficient understanding of the field or because too many variables are involved. A prominent example is studies using biomarkers in population health, where the biomarkers are presumably parts of physiological regulatory networks that are still poorly understood. In this case, an exploratory version of SEM is needed to define a limited subset of models that are in agreement with the data, and which can be further tested. Here, we present such a method using longitudinal biomarker data. The algorithm explores all identified three-variable models and uses these results to eliminate as many non-supported causal relationships as possible. The model then proceeds systematically through 4-variable and larger models, incorporating the results of the lower-order models. Gradually this process builds a consensus set of models in which all non-supported relationships have been eliminated, but which may still contain ambiguous relationships. These models can then be tested in independent or test data sets set aside for this purpose.

This paper deals with estimating a population that is largely defined by the fact that neither its size nor composition are readily accessible from census data in the U.S. and the other countries that use the De Jure concept of population. The population in question is that of de Facto population, which is the concept of people enumerated, estimated, or forecasted where they are found rather than where they usually reside. Estimating this type of population as well as its components is an important, but not easy task. In an effort to develop this field of population estimation more fully, we provide an equation to define the De Facto population and an example of its use. We describe and discuss each of the components of this equation and also provide examples of estimates of its direct components and an implied component – the daytime population. Although we view a population impacted by a disaster as distinct from a De Facto population, we include a discussion of it here since many of the methods used to estimate a De Facto population are applicable.

Cohort Change Ratios (CCRs) have a long history of use in demography, but they appear, however, to have been overlooked in regard to stable population theory. In this paper, we explore the use of CCRs as a tool for examining the idea of a stable population. Our exploration utilizes four case studies that represent current populations that have very difference dynamics. In comparing the approach using CCRs to the traditional approach, we note benefits and drawbacks. Benefits of the CCR approach include the ability to easily deal with both sexes, all of the components of change, including migration, and, importantly, an indication of how much time would be needed under a constant set of CCRs for a given population to become stable. To this end, we have developed an Index of Stability and demonstrate its use in the context of regression modeling.

In an ex post facto test of a modification of the Root Mean Square Error approach to developing 66% confidence intervals for population forecasts made using the Hamilton-Perry Method, we find that in a sample of nine states (one from each of the nine census division), that the intervals contain the 2010 population in 126 of 162 observations (77.8%) and similar results for an ex post facto test against 2000 data. We find the results encouraging. The paper describes the Root Mean Square Error approach as well as the data and the Hamilton-Perry Method.