Below you will find an overview of the core courses at INED in Paris. There are also four preparatory courses at the MPIDR in Rostock. You can find more information about these preparatory courses here.

Formal Demography (Course coordinator: José Manuel Aburto)

The student shall acquire practical knowledge of the important components of formal demography.

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  1. Learning outcomes

    On a general level the student shall acquire practical knowledge of the important components of formal demography. Specifically, students will be able to:
    • use infinitesimal, differential, integral, and matrix calculus in their future practice;
    • acquire an overview of usual formalization in demography.
    • apply formal demographic techniques empirically using R software.

  2. Course content

    The course is divided into four modules:
    • Introduction to population models
      The aim of this course is to present the mathematical theory underlying population growth (often known as stable population theory) and explore two approaches to this theory: the classical, continuous-time age-classified approach based on the life table, and the more recent discrete-time, age- or stage-classified approach using matrix models. The course will also begin to explore how the theory of survival analysis can be developed in terms of population models.
    • The Demography of Kinship
      Kinship is a fundamental property of human populations and a key form of social structure. Demographers have long been interested in the interplay between demographic change and family configuration. This has led to the development of sophisticated methodological and conceptual approaches for the study of kinship, some of which are reviewed in this course.
    • Decomposition Techniques
      The course will present essential methods for demographic analysis with a focus on decomposition techniques. Students will improve their skills by applying these methods in R.
    • Alternative Measures
      This formal demography course introduces alternative measures primarily within the field of mortality research. Some examples of what is covered include the mathematics and applications of: life expectancy, cohort perspective of mortality, and modal age at death.

  3. Assessment 

    The course is designed as a series of lectures and seminars. Grading is based on individual performance, via. written assignments, oral presentation or group activities.

Statistical Demography (Course coordinator: Carlo Giovanni Camarda)

The student shall acquire practical knowledge of the important components of statistical demography.

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  1. Learning outcomes

    On a general level the student shall acquire practical knowledge of the important components of statistical demography. Specifically, students will be able to:

    • Understand causal inference methods for observables and unobservables;
    • Gain conceptual knowledge of multilevel analysis and its applications;
    • Develop skills in event history analysis, including duration distributions and time-varying covariates;
    • Learn sequence analysis techniques using R (TraMineR), including data management and visualization.

  2. Course content

    The course is divided into several modules:
    • Causation
      This module aims to introduce students to principles of counterfactual causal inference, providing them with an overview of some methods in this field. Focus is placed on methods for conditioning on observables (i.e. regression adjustment, g-computation, propensity score methods, imputation methods for missing data), with a possibility, if there is interest, to also delve into methods for conditioning unobservables (i.e. individual intercepts, regression discontinuity, difference-in-differences, and instrumental variables).
    • Multilevel Data Analysis
      This module will teach you a basic conceptual understanding of the multilevel (also known as mixed or hierarchical) analysis. It will cover theoretical fundamentals of the multilevel approach to the data and show potential applications, paying particular attention to the solid understanding of key concepts (such as levels, fixed and random coefficients, variability or shrinkage), reasonable approach to model building, and interpretation of the results. Issues related to contextual effects, within-between models, and cross-level interactions will also be discussed. Further, the module will devote time to specific applications, such as repeated measures (panel data analysis) or non-linear models, and the Bayesian approach to multilevel analysis.
    • Event History Analysis
      In this series of three modules, you will explore event history models, powerful statistical techniques designed to analyze the timing of various events over time, such as death, marriage, childbirth, retirement, and more. The first module covers a broad range of topics, including the characterization of duration distributions and commonly used parametric families to accurately represent event durations. You will also examine observation schemes, including censoring and truncation, which play a vital role in analyzing event data. Nonparametric approaches will be explored as flexible alternatives to parametric models, allowing for a deeper understanding of complex event patterns. Throughout the second module, we will cover basic hazard regression, specifically proportional hazards models. These models investigate how different factors or covariates influence the hazard of events occurring. You will also delve into the Cox proportional hazards model, a widely used and powerful tool in event history analysis. Model diagnostics will be employed to assess the model's validity and fitness. A third module includes an introduction to discrete-time hazard regression models, which enable event analysis within discrete time intervals. Furthermore, you will explore the piece-wise constant hazard model, specifically applicable to aggregate event-data, facilitating the analysis of time-varying covariates. The course highlights the relationship between individual-based models and demographic models for aggregate data, providing insights into different modeling perspectives and their implications. Additionally, you will gain an understanding of the underlying assumptions and estimation methods used in basic demographic models. Time permitting, advanced topics such as non-proportional hazard models, unobserved heterogeneity, and competing risk models may also be covered.
    • Sequence Analysis
      The increasing availability of (longitudinal) data and the explosion of big data in demography and other social sciences present methodological challenges in relation to the significant reduction of information that allows the isolation and identification of crucial properties and relationships. At the same time, there is growing interest in understanding what are the relevant patterns in time-related processes that have recently gained complexity such as professional careers, family trajectories or migration pathways. This module aims to provide a concise introduction to sequence analysis, its origins and its applications, which will be demonstrated with hands-on practice using the software R (package TraMineR). We will cover basics of data management, major techniques of algorithmic sequence comparison, sequence visualization, grouping of sequences in typologies, as well as recent developments and applications in the social sciences.

  3. Assessment

    The course consists of modules that combine lectures and lab sessions. Evaluation for each module is based on individual performance, which may include written assignments, oral presentations, or participation in group activities.

Population Data Science (Course coordinators: Albert Esteve & Emilio Zagheni)

The student shall acquire practical knowledge of the use and calculation of summary measures using various data sources.

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  1. Learning outcomes

    On a general level the student shall acquire practical knowledge of the use and calculation of summary measures using various data sources. Specifically, students will be able to:
    • individually discuss and calculate basic summary measures
    • link fertility and mortality laws to population dynamics
    • use multistate life tables, compare standardization methods
    • understand the methods used in working with incomplete data
    • work on building and using consistent time series
    • use heterogeneous information in a consistent way
    • understand and discuss qualitative approach in demography

  2. Course content

    The course is divided into several modules:

    • Introduction to Demography. Data Quality and Types
      This module offers an overview to key concepts of data quality (e.g. accuracy, coherence, comparability, clarity) and applies them to three commonly used demographic data sources: registers, censuses and surveys. Sessions may revolve around discussions on: components of data quality and introduction to register data; population censuses and the IPUMS international project; and cross-sectional and longitudinal survey data.
    • Dealing with Data
      This module is all about data wrangling. Since students will have taken an introductory programming course as a pre-requisite, this module will allow students to extend their expertise of working with data in R. The module will not only go over necessary concepts for programming, but will mainly consist of working through examples of data wrangling in R. Tidyverse will be the main framework used to work with data.
    • Digital Demography
      The global spread of Internet, social media, and digital technologies is radically transforming the way we live and communicate, is creating new challenges and opportunities for our societies, and is enabling social scientists to address longstanding demographic research questions with new data sources—potentially requiring new conceptual and methodological approaches. With emphasis placed on population processes, discussions will be based around several substantive topics related to the emergence of (big) data-driven discovery in social sciences. The main goals of this module are to introduce students to: recent advancements made in the field of Digital and Computational Demography; some methods, approaches, and tools of data science in the context of population research; and to prompt critical thinking about modern demographic analysis and (online) data-driven discovery.
    • Demographic Estimation with Deficient and Limited Data
      This course aims to analyze mortality and fertility using limited or deficient data. It focuses on the challenges associated with estimating demographic parameters under such circumstances and provides an overview of key estimation methods. Through the utilization of survey and census data, participants will explore selected direct and indirect methods to assess levels and trends in under-five mortality, adult mortality, and fertility across multiple countries.
    • Qualitative Research in Demography
      The rise in qualitative contributions to research endeavors has been mirrored in Demography, traditionally a quantitative discipline. Since 1980s, populations studies scientists have increasingly used qualitative methods. The methods used have themselves shifted over tome, from ethnographic approaches, towards increasing reliance on focus groups and in-depths interviews. Qualitative approaches are used in demography at multiple points in the production of knowledge, including: design and testing of quantitative questionnaires; to understand unexpected survey results; and, to grasp sensitive issues, perceptions, “cultural contexts”, and other elements of the social world which are difficult to measure quantitatively.
    • Using Environmental Data from Remote Sensing in Demographic Analysis
      The nexus of population-environment is widely discussed. However, there are still considerable research gaps on the linkages between population and environment, due to difficulties in access and use of environmental data at desired spatial and temporal scales. Demographic data sources often include spatial information: administrative units for censuses and administrative data, cluster coordinates for surveys, etc. The goal of this short training course is to introduce the students to various openly available international environmental data sources stemming from remote sensing and their use for demography. During this course, we will discuss research questions linking population and environment, provide hands-on training on QGIS and R to link environmental and demographic data, creating environmental parameters as per the interests of the students.

  3. Assessment

    The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

Demographic Theories (Course coordinator: Brienna Perelli-Harris)

The student shall acquire a thorough knowledge of the theories and trends behind the causes of various demographic outcomes.

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  1. Learning outcomes

    On a general level the student shall acquire a thorough knowledge of the theories and trends behind the causes of various demographic outcomes. Specifically, students will be able to:
    • make use of theories to analyse changes in fertility, nuptiality, mortality and migration
    • make use of theories to analyse changes in long-term relationships between population development and living conditions
    • understand the precise mechanisms by which personal attributes, including the stage in the life course, and contextual factors, such as economic conditions and socio-cultural system, affect fertility, nuptiality, mortality and migration
    • understand the mechanisms by which events and conditions during one stage of life affect demographic events and behaviour later in life
    • prove a disprove a theory using falsifiable hypotheses
    • present a theoretically based analysis of the complex interplay between population change and economic and social development.

  2. Course content

    The aim of the course is to introduce students to macro-level theories of population change, micro-level theories of demographic behaviour and the micro-macro interactions. At the end of the courses, students should comprehend the major theories that explain the trends and patterns of fertility, family formation and dissolution, the ageing of individuals and society, migration behaviour and migration systems. These theories are situated within the overarching framework of the human life course, embedded in institutional contexts that reflect economic, social, cultural and historical conditions. In addition, students should understand the demographic transition and the demographic response to situational changes such as technological change, economic development, food shortage and economic crisis. Therefore, theories explaining both the influence of population growth on economic, social, and environmental development and vice-versa are discussed. Students should be able to apply these theories to interpret data on levels and differentials in demographic change and the drivers: fertility, mortality and migration, to identify how long-term and short-term economic changes influence population behaviour as well as to understand the complex interrelationships between population and living standards by using information with details at individual and family, and household levels.

    The course lasts 8 weeks and covers the following topics:

    Week 1. Fertility and family I
    • Trends and patterns in fertility around the world and over time: Fertility during the First Demographic Transition. Low and lowest-low fertility.
    • Explanations for contemporary fertility patterns: Cross-national comparisons; Historical, cultural, and economic explanations; Women’s employment and work-family balance; Family policies and institutions; The gender equality debate.
    • Trends and explanations for new family behaviors. Families and households. Marriage, cohabitation, divorce, and repartnering. The Second Demographic Transition as a set of behaviors. Cohabitation in-depth: Cultural and historical patterns; Pattern of Disadvantage; Policies and laws; Discourses about the meaning of cohabitation.
    • Theories of the Family: Why we need theory. Economic, cultural, social- psychological, and Institutional/Sociological theories of family change. Debate: Is the Second Demographic Transition spreading around the world?

    Week 2. Fertility and family II
    • Evaluating causes and consequences: Causation in family demography; Example 1: women’s employment and fertility, a micro-macro paradox; Example 2: education and second-birth risk, an artifact of selection; Example 3: cohabitation and intergenerational contacts; Example 4: the use of experiments in family demography
    • Antecedents and consequences of divorce: Trends and patterns of divorce across middle- and high-income countries; Review of major antecedents od union dissolution; The educational gradient in union dissolution; Women’s employment and divorce in different settings; Consequences of union dissolution: for partners, for children, for fertility. Debate: Are LAT relationships a new family form?
    • Subjective well-being and family behaviors. The notion of subjective well-being in family demography; Subjective well-being and union formation; Subjective well-being and union dissolution; Subjective well-being and fertility, a two- sided relationship. Debate: What are the consequences of divorce on children outcomes?

    Week 3. The life-course and inequality
    • The concept of the lifecourse and linked-lives, embedded in social and historical contexts.
    • Trends in Inequality: Inequality in Outcomes: Cross-national variation and change over time in income inequality; Welfare regimes; Wealth Inequality. Inequality of Opportunity: Social mobility, class schemes, cross-national variation in mobility.
    • Families, Schools and Neighborhoods: An overview of the main ambits within which inequality of opportunity is shaped. Families and Parenting; Education as the great equalizer; Educational Choices; Segregation; Neighborhood effects; Social Capital.
    • Demographic Change and Inequality. Diverging Destinies; Family Structure and Child Outcomes; Same-sex parent families; Trends in Homogamy; Homogamy and Inequality.

    Week 4. Mortality I
    • The epidemiologic transition: from communicable to non-communicable diseases, chronic diseases and health related behaviors.
    • The life expectancy revolution and the postponement of senescence: is there a limit to human lifespan?
    • Introduction to the evolutionary theory of aging and the biodemography of human aging.
    • Dynamics of population survival: introduction to heterogeneity, mortality deceleration and convergence, mortality improvements and tempo effects.
    • Mortality differentials within and across populations: sex and socioeconomic inequalities.
    • The COVID-19 pandemic: impact on life expectancy dynamics.
    • Beyond life expectancy: other indicators of lifespan length, mortality compression, and the relationship between lifespan length and lifespan inequality

    Week 5. Mortality II
    • Population aging: challenges
    • Theories of population aging and health: compression or expansion of morbidity?
    • Measuring population health: a difficult task
    • Aging populations: a challenge for pension systems
    • Aging populations: a challenge for the healthcare system
    • Family structure and dynamics in aging societies
    • Intergenerational transfer of health within multigenerational families

    Week 6. Migration I
    • Conceptual issues and definitions
    • Residential mobility versus migration; internal versus international migration
    • Theoretical concepts versus measurement
    • Theories of and approaches to internal migration: macro / micro / meso approaches, life course, human capital, migration and family

    Week 7. Migration II
    • Trends in international migration
    • Theories of international migration
    • Migration and development
    • Integration of immigrants’
    • Measurement issues in international migration
    • International migration in demography and other disciplines

    Week 8. Historical Demography
    • Intellectual foundations of Demography
    • The importance of sources and its political foundations
    • Lon-term changes in populations
    • The role of different actors –individuals and families but also the state, various institutions, and demographers themselves– on the way population issues are shaped, discussed, and understood by societies

  3. Assessment

    An assignment will be given to the students each week. The format of the assignment can differ from one week to another, but we recommend either a series of exercises, a quiz or a short essay.

      Population challenges (Course coordinator: Tommy Bengtsson)

      The student will acquire knowledge of the main consequences of demographic change, making a distinction between the situation in the past and today.

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      1. Learning outcomes

        On a general level the student will acquire knowledge of the main consequences of demographic change, making a distinction between the situation in the past and today. They will get more detailed insights into policy reforms aimed at meeting the challenges posed by population ageing and depopulation.

        More specifically, to pass the assessments students will be able to:
        • understand the impact of population growth on economic and environmental development, historically and today,
        • analyse the effects of long-term changes in fertility, mortality, and migration on the different stages of population ageing and depopulation,
        • analyse the differences in income and consumption over the life cycle and the need for private and public generational transfers, monetary and/or in kind, and generational fairness,
        • analyse the effects of population ageing on the financial stability of health and elderly care arrangements, and pension systems, and
        • analyse the need and opportunities for policy interventions aimed at improving labour supply and creating fiscal stability of old age, health care, and pension systems.

      2. Course content

        The course is divided into three modules:
        • Population, economic development, and the environment
          The first week gives an overview of population debates starting in the mid-twentieth century and its intellectual roots. It includes theories of the effects of population growth on consumption and savings, on the demand for raw materials and on the environment, and gives empirical examples. It then turns to population ageing and depopulation and discusses the roles of fertility, mortality, and migration during different stages of this development. Finally, it identifies challenges of population ageing and depopulation for different welfare state systems and discuss possible solutions at a general level.
        • The generational economy
          The second week focuses on the differences in income and consumption over the life cycle and the need for private and public generational transfers, monetary and/or in kind. Such transfers are becoming more and more important not only because of an ageing population, but also because the per capita costs for old-age care, health care, and pensions have increased in recent years. The generational perspective, based on National Transfer Accounts and time use data, will we used to get a deeper understanding of the need for such transfers and their actual flows. Special attention will be given to solutions provided by families and the welfare state and generational fairness.
        • Ageing and public policies
          The last week focuses on the role of political interventions aimed at making existing welfare systems financially stable, with a focus on Europe. It includes interventions to get more people into the labour force and making them work longer such as vocational training, work practice, and adult learning. Special attention is given to how pension reforms are designed, implemented, and evaluated. Other policy interventions aimed at making old age and health care systems more efficient and financially stable will be discussed. Special attention is given to labour force participation, income attainment, and pensions of women.

      3. Assessment

        The course is designed as a series of lectures, exercises, and work project reports. Grading is based on individual performance, via written exams, papers, presentations, and other mandatory activities.

      Simulation and Forecasting (Course coordinator: Marie-Pier Bergeron Boucher)

      The student shall acquire practical knowledge of the modeling, simulation and forecasting of various populations.

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      1. Learning outcomes

        On a general level the student shall acquire practical knowledge of the modeling, simulation and forecasting of various populations. Specifically, students will be able to:
        • analyse the dynamics of age-structured and of interacting populations
        • learn about new indicators of aging and how to evaluate them
        • learn how to prepare initial data for population projection (life table extension, smoothing age-specific fertility and mortality rates)
        • forecast population development using the cohort component approach
        • learn how to define scenarios in terms of aggregate indicators and apply demographic models in order to obtain age-specific rates
        • apply household projection methods
        • individually simulate multi-state populations
        • discuss the fundamentals of microsimulation models

      2. Course content

        • Population Projections
          Population Projections is probably the demographic tool that is most frequently accessed by professionals outside the field of demography. Accurate population projections play a crucial role in informing policy planning by providing the future size and composition of populations. This module provides a study of population projections methods and applications. Two methods will be discussed during the module: (1) the constant exponential growth model, and (2) the cohort component method. The module will also focus on the use of variable-r relations to estimate the population size when data is not accurate. The lectures will combine theoretical concepts with applications using R.
        • Demographic Forecasting
          Accurate population projections rely on forecasting fertility, mortality, and migration patterns. This course offers an introduction to demographic forecasting, focusing on key methodologies employed for this purpose. Some examples include parametric curve models and the Lee-Carter method. More advanced methodologies will be covered if time allows. The lectures will be interactive, combining theoretical concepts with hands-on exercises. The programming implementation of the various methods will be illustrated using R. Students will also receive an introduction to dynamic visualizations (i.e. animategraphics, gganimate, and .gifs) and shiny apps.
        • Mortality Disturbances: Age-Period-Cohort Modeling and Visualization
          The module primarily focuses on understanding the significant changes and disruptions in mortality that have occurred over the past 300 years. The module will examine the key theories and approaches formulated to analyze these changes, as well as explore the main conceptual and methodological challenges involved in studying mortality changes and disturbances. Special attention will be given to the age-period-cohort analysis of mortality changes, including its capabilities, potential misuses, and limitations.
          During the practical section of the module, students will utilize R scripts to review and apply basic visual and statistical methods for analyzing mortality changes. Emphasis will be placed on decomposing mortality changes into the age, period, and cohort dimensions, and on measuring excess mortality. This hands-on approach will allow participants to gain practical experience in applying these techniques.
        • Agent-based Modelling and Simulation
          The aim of the module is to connect microsimulation, agent-based modeling (ABM), and probability theory. Microsimulation models and ABM rely on random variables and their probability distributions. One particular function, the quantile function or inverse distribution function, is the workhorse of both microsimulation and ABM. Different quantile functions are covered and their use in stochastic agent-based modeling discussed. Illustrations are in R.

      3. Assessment

        The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

      Thesis Course (Course coordinator: Giulia Ferrari)

      The course is designed as a series of lectures and seminars, focusing on individual performance assessment through oral presentations and group activities.

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      1. Learning outcomes

        By the end of the course, students will be able to:
        • Identify a relevant research question
        • Frame the research aims and goals of an independent doctoral study
        • Prepare a well-structured thesis proposal
        • Present a written report, adhering to academic standards, describing their research
        • Discuss research reports based on academic standards
        • Deliver a scientific presentation
        • Create a scientific poster
        • Write a well-structured scientific paper, select an appropriate journal for submission, and make revisions based on peer reviews

      2. Course content

        The course consists of three main parts:

        1.  Progress Presentations: Approximately half of the classes will be dedicated to students presenting the progress of their thesis work. This work is expected to be completed by the end of the program. Students will define a research issue, conduct independent research, and write their thesis with support from a supervisor. The supervisor will guide the development from idea to plan and provide feedback throughout the process. A thesis proposal/research paper should consist of original, independently executed work. The general structure of the proposal's content should include: (1) Specific aims; (2) Background and significance; (3) Preliminary studies; (4) Research design and methods. The general structure of the research paper should include: (1) Introduction; (2) Data and Methods; (3) Results; (4) Summary and Discussion.

        2. Research Seminars: Approximately a quarter of the classes will feature seminars conducted by either the weekly EDSD instructor or other local researchers. The aim is to expand students' knowledge about current research topics in demography.

        3.  Soft-skills Workshops: Approximately a quarter of the classes will be dedicated to soft-skills workshops aimed at acquiring crucial skills for demographic research. These workshops will cover topics such as research paper writing, presentation and poster creation, accessing survey and contextual demographic data, research project design, applying for research funding, as well as Latex and cartography tools.

      3. Assessment

        Teaching primarily takes place through individual supervision and discussions within the student group during seminars. The examiner will provide guidance and feedback at different phases of the project. Throughout the writing process, students can consult their supervisor for advice, feedback, and criticism.