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, starting from the key concepts of demographic analysis, gradually face 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.

In the first part of the course (Key concepts in demography), students will familiarize with key 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 main demographic trends in Italy and Europe.

The second part of the course (Regression modelling in socio-demographic analysis) aims at introduce 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. The wide availability of data in our societies allows us to analyze several issues related to demographic and social spheres. In order to exploit data sources, the knowledge of statistical analysis and inference is a crucial point. Students will also develop specific skills in order to face selection bias and endogeneity in regression modelling.

The third part of the course (Life Course Analysis) introduces quantitative life course analysis. The usefulness of longitudinal data and hazard models for casual 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.

During the course, we will address some key demographic issues such as population aging, low fertility, human migration, the transition to adult and diffusion of new forms of union. Students are involved in computer programming (using STATA software) for each topic covered in the second and third part of the course. 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

1. Key concepts in Demography

What is Demography? The population growth. The population momentum. Crude rates. The population equation. Age structure. Age pyramids. Sex structure. Cohort and period measures. Age-specific rates and probabilities. Age standardization. The Lexis diagram. Probabilities. Fertility measures. The Total Fertility rate. Life tables and life expectancy.

2. Regression modelling in socio-demographic 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. Facing selectivity and endogeneity in regression modelling. The Heckman’s model.

3. Life Course Analysis

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. The episode splitting. Going beyond the basic approach: developments in the EHA and other life course analysis.

A “holistic” perspective: the sequence analysis. Dissimilarity matrix and cluster analysis applied to sequences.

4. Key issues

The population ageing process. The demographic transitions. Global international migration. Causes and consequences of international migration. Second generation of immigrants. Student mobility. The transition to adulthood in Western countries. Family formation and patterns of cohabitation in Europe. Marital disruption. Lowest low fertility in Italy.

Readings/Bibliography

Teaching methods

The course consists of lectures, readings and discussion in class. Class attendance is strongly recommended to successfully complete this course.

Class activities (48 h) including:

• lectures (40 h)
• mid-term exam (2 h)
• Meetings for presentation and discussion of research report developed by groups (4 h)
• Seminars (2 h, tbc)

Lab (18 h) including:

• Practises on methods and models in Social Demography (with TEACHER) (12 h)
• STATA basic features and data manipulation (with TUTOR) (2 h)
• EHA exercises (with TUTOR) (2 h)
• Test on EHA basics (2 h)

Assessment methods

Requirements:

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

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

 

Assessment method (for attending students)

• Midterm exam (5 pt): written test including some exercises on key concepts in demography.
• Research paper (10 pt): developed by small groups (2/3 students) and presented at the end of the course
• EHA exercise (5 pt): to be made in the computer lab at the end of the course (see Calendar)
• Final exam (10 pt): oral examination on all the contents of the course (see Calendar)

Please note that the points acquired remain valid until the end of academic year (i.e. February of the subsequent year). Exams and activities are not compulsory. However, the sum must be at least 18 in order to pass the exam.

 

Assessment method (for non-attending students)

Oral exam (20 pt) + Research paper (10 pt). The report must be made individually (if the student does not achieve a sufficient grade, the paper remains valid until the end of academic year, i.e. February of the subsequent year).

Teaching tools

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

Use of computing equipment for statistical analysis (STATA software).

Office hours

See the website of Roberto Impicciatore