Demography

Objective

Classic demography is formal in its essence and describes with mathematical means the empirical structure of a populations and their dynamics shaped by aging, fertility, mortality and migration. Social demography goes beyond the statistical description of a population and creates an interdisciplinary link to other, theoretically informed behavioural sciences with the goal to explain central demographic and socioeconomic phenomena with social behaviour and beyond.

In this course, we will learn about several important explanations for demographic behaviour and trends which are located on the population level, the intermetidate level, the individual level (macro-meso-micro) and even down to the molecular genetic level, which recently has been integrated in this field. We will learn, amongst others, about social networks effects, the importance of neighbourhoods, technological advancements and assume a life course perspective for the study of processes such as aging or the development of early life experiences. All of these social factors might interaction with an individual’s genome. Finally, we will discuss several (newly) available data sources including those, which stem from recent computational efforts tracing internet data from facebook or twitter. 

The students are expected to have an advanced knowledge of empirical social research and applied econometrics. After attending this course, students should be equipped with important analytical tools to understand the connection between the social world and central population and developmental indicators and have an overview over the current research frontiers.

Planning

Week 1: What is Social Demography? (Theory)

Week 2: Social Demography and Causality (Theory)

Week 3: Family and the life course (Methods: Event history analysis and sequence analysis)

Week 4: Fertility and Sexuality (Methods: Fixed-effects regression analysis)

Week 5: Health, Morbidity and Mortality (Methods: Event history analysis)

Week 6: New Demographic Thinking and Data (Methods: GWAS, Computational Methods)

References