Mechanisms of family formation:an application of Hidden Markov Models to a life course process

Life courses consist of complex patterns of correlated events and spells. The nature and strength of these correlations is known to depend on both micro- and macrocovariates. Life-course models such as event-history analysis and sequence analysis are not well equipped to deal with the processual and latent character of the decision- making process. We argue that Hidden Markov Models satisfy the requirements of a life course model. To illustrate their usefulness, this study will use Hidden Markov chains to model trajectories of family formation. We used data from the Generations and Gender Programme to estimate Hidden Markov Models. The results show the potential of this approach to unravel the mechanisms underlying life-course decision making and how these processes differ both by gender and education

Longitudinal methods for life course research : a comparison of sequence analysis, latent class growth models, and multi-state event history models for studying partnership transitions

Júlia Mikolai was a PhD student at the Department of Social Statistics and Demography at the University of Southampton and was funded by a +3 Scholarship provided by the Economic and Social Research Council (ES/J500161/1) while completing most of this work.This paper qualitatively compares and contrasts three methods that are useful for life course researchers; the more widely used sequence analysis, and the promising but less often applied latent class growth models, and multi-state event history models. The strengths and weaknesses of each method are highlighted by applying them to the same empirical problem. Using data from the Norwegian Generations and Gender Survey, changes in the partnership status of women born between 1955 and 1964 are modelled, with education as the primary covariate of interest. We show that latent class growth models and multi-state event history models are a useful addition to life course researchers’ methodological toolkit and that these methods can address certain research questions better than the more commonly applied sequence analysis or simple event history analysis.PostprintPeer reviewe

Preconference Training Workshop: Multistate analysis of life histories with R

Multistate Analysis of Life Histories with R Frans Willekens, Netherlands Interdisciplinary Demographic Institute and Max Planck Institute for Demographic Research Workshop Venue: Rideau Salon Workshop Outline A. Introduction Multistate models describe the life course in terms of transitions individuals experience as they go through stages of life and move between states. These states may represent health states, family status, occupation, place of residence, education or other domains of life. Multistate models have been successfully used in a wide variety of applied sciences. The most fruitful areas of application are health sciences, demography and economics. Important examples of applications of multistate models are stem cell transplantation (with disease relapse and death as endpoints and graft-versus-host disease as intermediate states), estimation of healthy life expectancy, marital careers, migration histories, and participation in the labor market. These applications have in common a fundamental interest in competing risks, event histories and state sequences. The subject of the workshop is the modeling of life histories. Multistate analysis of life histories with R is an introduction to multistate event history analysis. It is an extension of survival analysis, in which a single terminal event (endpoint) is considered and the time-to-event is studied. Life histories are modeled as realizations of continuous-time Markov processes (and extensions). The statistical theory of counting processes emerged as the dominant theory for estimating transition rates from data on event counts and populations at risk. Non-parametric and parametric methods have been developed. In recent years, software packages for multistate modeling have become available. R is the language of choice and the Comprehensive R Archive Network (CRAN) is the main repository. The packages are free and the source code is available in CRAN. The packages include survival, eha, Epi, mvna, etm, mstate, msm, Biograph, MicSim andTraMineR. For a recent review, see Willekens and Putter (2014) Software for multistate analysis, Demographic Research 31(14):381-420, and the CRAN Task View on Survival Analysis In 2011, the Journal of Statistical Software published a special issue on multistate modeling (H. Putter, editor). Multistate modeling is an active area of research across disciplines. The research benefits from the current interest in prognostic modeling (of outcomes of health conditions and behavior/lifestyle) and predictive analytics

A discussion on hidden Markov models for life course data

This is an introduction on discrete-time Hidden Markov models (HMM) for longitudinal data analysis in population and life course studies. In the Markovian perspective, life trajectories are considered as the result of a stochastic process in which the probability of occurrence of a particular state or event depends on the sequence of states observed so far. Markovian models are used to analyze the transition process between successive states. Starting from the traditional formulation of a first-order discrete-time Markov chain where each state is liked to the next one, we present the hidden Markov models where the current response is driven by a latent variable that follows a Markov process. The paper presents also a simple way of handling categorical covariates to capture the effect of external factors on the transition probabilities and existing software are briefly overviewed. Empirical illustrations using data on self reported health demonstrate the relevance of the different extensions for life course analysis

Fertility Decisions in the FRG and GDR: An Analysis with Data from the German Fertility and Family Survey

The aim of this paper is to compare family policies and fertility patterns in the former German Democratic Republic (GDR) and the German Federal Republic (FRG). Among other aspects, both societies particularly differed in the integration of women into the labor market. By contrasting the fertility development in these two societies, this paper aims to illuminate how women’s education and employment relates to fertility decisions in societal contexts that support (in the case of the GDR) and hamper (in the case of the FRG) the compatibility between work and family life. Data for this analysis comes from the German Fertility and Family Survey (of the year 1992). We provide descriptive statistics for all birth parities, but we limit the multivariate event history analysis to first births only.event history, event history analysis, first birth, former GDR, Germany, life-course

Causal Effects of the Timing of Life-course Events Age at Retirement and Subsequent Health

In this article, we combine the extensive literature on the analysis of life-course trajectories as sequences with the literature on causal inference and propose a new matching approach to investigate the causal effect of the timing of life-course events on subsequent outcomes. Our matching approach takes into account pre-event confounders that are both time-independent and time-dependent as well as life-course trajectories. After matching, treated and control individuals can be compared using standard statistical tests or regression models. We apply our approach to the study of the consequences of the age at retirement on subsequent health outcomes, using a unique data set from Swedish administrative registers. Once selectivity in the timing of retirement is taken into account, effects on hospitalization are small, while early retirement has negative effects on survival. Our approach also allows for heterogeneous treatment effects. We show that the effects of early retirement differ according to preretirement income, with higher income individuals tending to benefit from early retirement, while the opposite is true for individuals with lower income

Conceptual model to explain turning points in travel behavior: Application to bicycle use

Existing knowledge on cycling behavior, as with travel behavior in general, is based mainly on cross-sectional studies. It is questionable how much can be learned about the reasons for behavioral change from such studies. A major investment program to promote cycling in 12 English cities and towns between 2008 and 2011 provided the opportunity to study the bicycle use of residents and how that use was affected by the investment. Face-to-face interviews collected biographical information on travel behavior and life-change events during the investment period for 144 research participants and probed the reasons for changes in bicycle use. Theory (from the life course perspective) and preliminary analysis of the interview data were used to develop a conceptual model that hypothesized that turning points in travel behavior were triggered by contextual change (a life-change event or change in the external environment) and mediated by intrinsic motivations, facilitating conditions, and personal history. The model provided an effective means of explaining turning points in bicycle use. The analysis of the interview data showed how the nature of behavioral influences (in particular, life-change events and intrinsic motivations) varied over the life course. The research highlights the advantages of viewing travel behavior change in the context of people’s evolving lives and how that approach can help in developing transport policies and practices

Predicting the Timing of Social Transitions from Personal, Social and Socio-Economic Resources of German Adolescents

Social transitions are characterized by an increased heterogeneity in Western societies. Following the life course perspective, individual agency becomes central in shaping one’s life course. This article examines social transitions of adolescents using individual resource theory to explain differences of the timing of five transitions in partnership and family formation: the first sexual experience, the first intimate relationship, the first cohabitation, the first marriage, and the birth of the first child. Since little is so far known about how individual characteristics interact and influence the social transition to adulthood, we focus on the varying impacts of personal, social and socio-economic resources across the social life course. We use longitudinal data from the German LifE-Study, which focuses on the birth cohort of individuals born between 1965 and 1967. Using event history analysis, we find that the timing of the first sexual experience and first partnership transitions are mainly influenced by personal and social ressources, whereas socio-economic resources offer better explanations for the timing of entering marriage and parenthood. Most striking are the different explanatory models for women and men

Predicting the Timing of Social Transitions from Personal, Social and Socio-Economic Resources of German Adolescents

Social transitions are characterized by an increased heterogeneity in Western societies. Following the life course perspective, individual agency becomes central in shaping one’s life course. This article examines social transitions of adolescents using individual resource theory to explain differences of the timing of five transitions in partnership and family formation: the first sexual experience, the first intimate relationship, the first cohabitation, the first marriage, and the birth of the first child. Since little is so far known about how individual characteristics interact and influence the social transition to adulthood, we focus on the varying impacts of personal, social and socio-economic resources across the social life course. We use longitudinal data from the German LifE-Study, which focuses on the birth cohort of individuals born between 1965 and 1967. Using event history analysis, we find that the timing of the first sexual experience and first partnership transitions are mainly influenced by personal and social ressources, whereas socio-economic resources offer better explanations for the timing of entering marriage and parenthood. Most striking are the different explanatory models for women and men