87479 - Social Demography

Learning outcomes

By the end of the course, the student develops both awareness of the range issues considered in population studies and specific skills in multivariate regression analysis and life course analysis. In particular, the student will be able: to assess and apply the main demographic measures; to collect data from the major social and demographic surveys at an international level; to apply the appropriate statistical research strategy; to critically evaluate the results of the data analysis by connecting them to a suitable socio-demographic theory; and to deal with selection bias and endogeneity in regression modelling.

Course contents

Course Objectives

The course is an overview of population studies and social demography that gradually faces major debates and recent research results. Students should come away with the class with an awareness of the range issues considered in population studies and specific skills in multivariate regression analysis and life course analysis.

The wide availability of data in our societies allows us to analyse several issues related to demographic and social spheres. Thus, the knowledge of statistical analysis and inference is a crucial point.

The first module of the course (regression modelling and life course analysis) aims at introducing the logic of quantitative analysis in social and demographic research by providing students with skills for multivariate statistical analysis also including multi-process modelling and unobserved heterogeneity components to account for selection bias and endogeneity in regression modelling. A special attention is paid to the life course approach. The usefulness of longitudinal data and hazard models for causal analysis in the social sciences is emphasized. Starting from the key concepts of life course approach in the quantitative analysis, basic elements to organize event-oriented data, develop multivariate models and interpreting results are discussed.

In the second module (key concepts in Demography), students familiarize with basic concepts of demographic methods and measures mainly related to population growth, age and sex distribution, fertility, mortality and migration dynamics. The main population theories are considered as well as a description of the major demographic trends in Italy and Europe.

During the course, we address some key demographic issues such as population aging, low fertility, migration, gender system, transition to adult and new patterns in family formation. Students are involved in computer programming (using STATA and R software). Moreover, the course enables students to structure and conduct autonomously a research project based on the analysis of data sets concerning social sciences.

At the end of the course, the student will be able to:

• assess and apply the main demographic measures and population development indicators;
• collect data from the major demographic and social surveys at international level;
• apply the appropriate statistical research strategy for the analysis of data coming;
• critically evaluate the results of the data analysis by inserting them within a suitable socio-demographic theory;
• check the assumptions on which each analysis depends and make appropriate adjustments or select alternative methods of analysis.

Course contents

Module 1 (Regression modelling and life course analysis)

Data sources for social and demographic research. Micro and macro approach. Causal explanation and multivariate analysis. Spurious effects. Prior and intervening variables. Regression model specification. Linear and logistic regression models. Multi-process modelling and unobserved heterogeneity components. Selectivity and endogeneity in regression modelling.

The powerful of longitudinal data. Causal modelling and observation plans. The relevance of the past. Data for the life course analysis. Computer programs for life course analysis. Event history data structures. Key concepts and basic terminology. Censoring and truncation. Nonparametric survival analysis. Life table method. Survival curves. Kaplan-Meier estimator. Exponential hazard model. Piecewise constant exponential models. Time fixed and time-varying variables. Restructuring data for the time-varying covariates. Going beyond the basic approach: developments in the EHA and other life course analysis.

Module 2. Key concepts in Demography

Population growth. Population momentum. Population equation. Crude rates. Age standardization. Age and sex structure. Age and sex pyramids. Lexis diagram. Cohort and period measures. Age-specific rates and probabilities. Fertility measures and Total Fertility rate.. Life tables and mortality models (Gompertz, Siler, Heligman and Pollard). Demographic transition. Demographic forecasts (synthetic and analytical methods).

Readings/Bibliography

Module 1

• Lesson notes, selected papers, exercises, datasets and documents (available on the VIRTUALE platform

Optional readings

• Mills M. (2011), Introducing Survival and Event History Analysis, Sage. (chapters 1-10)
• Blossfeld H-P., Golsch K., Rohwer G. (2007) Event history analysis with Stata. Mahwah (NJ): Erlbaum (chapters 1-6)

Module 2

• Lesson notes, selected papers, exercises, datasets and documents (available on the VIRTUALE platform
• PRB's Population Handbook (6th edition, 2011). Free download HERE

Optional readings

• Rowland D. T. (2003), Demographic methods and concepts, Oxford University Press (ch. 1, 2,3,4,6,7)

Teaching methods

Class activities includes lectures, activities on data manipulation, exercises, laboratories and practices on method and models in Social Demography, presentation and discussion of research report developed by students. Attendance is strongly recommended to successfully complete this course.

Assessment methods

Requirements

Students should be familiar with basic knowledge in the field of statistics and computer science. A basic knowledge of STATA and R is suggested.

Students are required to develop a research project based on real data.

Assessment method (for attending students)

• Midterm exam (5 pt): written test related to the contents covered in Module 1.
• Research paper (10 pt): developed by small groups (2/3 students) and presented at the end of the course.
• Computer test (15 pt): exercise related to the contents covered in Module 2.

Please note that the points acquired in the mid-term exam and research paper remain valid until the end of September.

Assessment method (for non-attending students)

Computer test related to the contents covered in Module 2 (15 pt) + oral exam related to the contents covered in Module 1 (15 pt)

Teaching tools

Online material (slides, papers, exercises, datasets, etc.).

Use of computing equipment for statistical analysis (STATA and R software). Authorized students can download STATA software and the related license HERE .
R is a free software environment that can be download at the following page: https://cran.r-project.org/ . To facilitate the use of R, the installation of R studio is recommended, which can be freely downloaded at the following link: https://www.rstudio.com/products/rstudio/download/ .

Office hours

See the website of Roberto Impicciatore

See the website of Lucia Zanotto