Formació

European Doctoral School of Demography

ABOUT



The EDSD is an eleven-month sponsored program that is offered every year aiming to provide students with an appropriate high-level education in demography to pursue their doctoral studies. Students will acquire a solid knowledge base on the causes and consequences of demographic change, population data, statistical and mathematical demography, as well as modeling, simulation, and forecasting. Many of the School’s courses concentrate on strengthening the quantitative and programming skills of the students. The language of the School is English.

In the 2021/22 academic year, the School will be held at two different locations. The preparatory courses (from early September to late October 2021) will be offered at the Max Planck Institute for Demographic Research in Rostock, Germany. The core courses (from November 2021 until the end of July 2022) will be held at the Centre for Demographic Studies (Centre d’Estudis Demogràfics – CED) at the Universitat Autònoma de Barcelona, Spain.

The EDSD does not charge fees. Several European research institutes and universities provide full fellowships for all students accepted to the program. Each student is matched with one of the Institutions within the EDSD network and received a monthly stipend, plus travel grants. Our participant Institutions are Max Planck Institute for Demographic ResearchInterdisciplinary Centre on Population Dynamics, Centre for Demographic Studies, Institut National d’Études Démographiques, Netherlands Interdisciplinary Demographic Institute, Sapienza Universitá di Roma, Lund University, London School of Hygiene and Tropical Medicine, Vienna Institute of Demography, Warsaw School of Economics, University of Groningen, University of Rostock, Tallinn University, Stockholm University, University of Southampton, Université Catholique de Louvain, London School of Economics.

Masters Degree in Demography

Successful completion of the School results in a European Research Certificate of Demography provided under the auspices of the European Association for Population Studies (EAPS).

Upon successful completion of the program and presentation of a thesis, the students enrolled in the EDSD will receive an official European Master in Demography delivered by the Universitat Autònoma de Barcelona.

Contact information: edsd@ced.uab.es

Preparatory courses in Rostock

The courses in Rostock consist of (1) Measures and Models in Demography, (2) Basic Mathematics for Demographers, (3) Basic Statistics for Demographers, and (4) Computer Programming for Demographers.

The aim of the preparatory courses is to prepare the students for the core courses at CED in Barcelona.

Core courses in Barcelona

The program at CED consists of seven courses on (1) Population Data and Science, (2) Demographic Theories: causes and consequences, (3) Statistical Demography, (4) Current Population Issues, (5) Mathematical Demography and (6) Demographic Modeling, Simulation and Forecasting, (7) Thesis courses. In addition, computer, graphic, presentation, and writing skills are taught. Students devote 20% of their time to thesis research.

Students will be also offered the opportunity to attend CED’s Social statistics and Population Seminars held weekly.

Courses


1. Mathematical Demography

  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.
  2. Course content
    The course is divided into four modules:

    • Stable Populations
    • 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.
    • Heterogeneous Populations
      Unobserved heterogeneity plays an important role in shaping mortality trajectories for populations. The aim of this course is to present t the key mathematical relationships that hold in models with unobserved heterogeneity. The course starts with the general case and introduce increasingly restrictive assumptions about the distributions of baseline mortality and unobserved heterogeneity. It presents short proofs and derivations as well as some brief qualitative interpretations.
    • 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
  4. 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.


2. Statistical Demography

  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.
  2. Course content
    The course is divided into sever modules:
    • Causation
      The course 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).
    • Event History Analysis
      During these courses, students 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 and acquire an overview of usual formalization in demography. The module will deal primarily with so-called event history models, statistical techniques used to analyze the occurrence of events in time, such as death, marriage, childbirth, entry into retirement, etc. The following topics will be covered:

      • Characterizing duration distributions and common parametric families
      • Observation schemes: censoring and truncation
      • Nonparametric approaches
      • Basic hazard regression (proportional hazards)
      • The Cox PH model, model diagnostics
      • Discrete-time hazard regression
      • The piece-wise constant hazard model; aggregate event-data
      • Non-proportional hazards models
      • Unobserved heterogeneity, repeated events, competing risks, multistate models
    • 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. The course 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). The course covers 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.
    • Qualitative Research in Demography
      The rise in qualitative contributions to research endeavours 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.
    • Multilevel Data Analysis
      This course will teach you a basic conceptual understanding of the multilevel (also known as mixed or hierarchical) analysis. The seminars 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 course will devote time to specific applications, such as repeated measures (panel data analysis) or non-linear models, and the Bayesian approach to multilevel analysis.
    • Agent-based Modelling and Simulation
      The aim of the course 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 or group activities.


3. Demographic Theories

  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 is divided into four modules and covers the following topics:

    1. Theories of Fertility, Family and the Life Course
      • 1. Major trends in fertility and the family and cross-national differences (i.e. low fertility, LAT, divorce, etc.)
      • Natural fertility and proximate determinants of fertility
      • The impact of values, norms and economics on fertility behaviour
      • The impact of gender policies, labour market policies and institutional arrangements (e.g. child care) on fertility behaviour
      • Theories of the family and family dynamics (including the Second Demographic Transition, intergenerational transfers of values, norms and resources)
      • Causation in the field of family demography
      • Consequences of family change on subjective well-being
    2. Theories of Mortality and Morbidity
      • Major trends in life expectancy, health expectancy, diseases and causes of death
      • Facts, trends, and potential explanations of (gender, socio-economic, etc.) differences in mortality
      • Introduction to biodemography of human aging; dynamics of heterogenous populations and mortality plateau; and mortality improvements and tempo effects
      • Theories of the epidemiologic transition
      • Trends and theoretical frameworks of healthy life expectancy and other indicators of lifespan length
      • Compression and expansion of morbidity: impact of lifestyle, environmental, socio- economic and cultural factors (including institutional factors, such as characteristics of the health care system)
    3. Migration
      This module comprises several courses which cover different, but essential, aspects surrounding the field of migration studies.

      • Theories of Migration: During the lectures, the students will get to know some main theoretical approaches of migration and residential mobility, derived from various disciplines (geography, demography, economics). Students will work actively with the theories by discussing the implications of the theories for empirical research.
      • Consequences of International Migration: The goal of this course is to explore the consequences of international migration. This will be done by covering the main methodological challenges of the study of international migration, giving an overview of immigrants’ integration theories and measurements, and emphasizing the links between international migration and demography.
      • Migration, Health, and Mortality in Developing Countries: This course endeavours to explain the relationship between migration (internal or international), health, and mortality in Low and Middle Income Countries (LMIC), with occasional references to More Developed Countries (MDC). Concepts, theories and hypotheses will be presented and some case studies will be discussed and confronted to theories and related hypotheses. Students can expect to understand and use the main concepts and terminology in migration and health studies; to understand the main hypotheses regarding the impact of migration on health; to know about the main sources of data on migration and their limitations; and to understand papers on migration and health through expanding upon its methodologies, results, and discussions.
    4. Historical Demography
      This course deals with the evolution of population in the long run, particularly in relation to economic development. By relying both on standard works in the field and on the most recent studies in historical demography and economic history, the course aims to give a broad but critical perspective on the evolution of population, and demography itself, over time and also discuss its relation with the subsequent emergence of the modern economy. The seminars aim to address the larger question of the role of different actors – individuals and families but also the state, various institutions, and demographers themselves—on the way the population issues are shaped, discussed, and understood by: introducing the intellectual foundations of demography, discussing the importance of sources for demography, by presenting a (concise) history of demographic sources and their political foundations, and exploring the evolution of population itself, with an emphasis on long term changes and the mechanisms underlying them.
  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.


4. Population Data and Science

  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 three 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.
  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.


5. Current Population Issues

  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
    The course is divided into three modules:

    • Social Indicators: Theory and Applications
      This course reviews some of the most popular indicators that are used to describe the social and structural conditions under which most demographic phenomena unfold. Starting with well-known inequality and poverty indicators, the course also explores the construction of composite indices and a variegated set of measures that are used to assess the extent of heterogeneity among populations.
    • Measuring the Generational Economy
      The course explores the question: How can we use economic data to analyse the relation between demography and economics? The course is organized based on the framework of National Accounts and its applications, specifically regarding the organization of the generational economy, demography and production/growth, and generational equity.
    • Demography and Inequality
      Income and wealth inequality have received renewed interest by social scientists over the last two decades. In many countries, both have increased dramatically and have been held responsible for a wide variety of societal developments. Therefore, this module reviews and discusses trends in economic inequality in OECD countries and discusses some of the narratives that offered to explain these. Different aspects and domains contributing to the inequality of opportunity are considered—some examples include: income and occupational mobility across generations; families, schools, and neighborhoods; and changing family dynamics in people’s life courses during the post-war period.
  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.


6. Moddeling. Simulation and Forescasting

  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 and Forecasts
      This course provides an introduction to population projections by discussing the constant exponential growth model, also spending time on the cohort component method and matrix projections. The lectures are interactive and R will be used to go over relevant examples and assignments for a more hands-on experience with the methods. Students also receive an introduction to dynamic visualizations (i.e. animategraphics, gganimate, and .gifs) and shiny apps. In terms of population forecasting, several topics may be explored and applied during lectures: the difference between population projections and forecasting, Lee-Carter modelling and forecasting, functional forecasting, and coherent forecasting—taking another population into account.
  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.

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7. Thesis course

  1. Learning outcomes
    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.

    • 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, in accordance with academic standards, describing their research
    • discuss, on the basis of academic standards, research reports
    • give a scientific presentation
    • prepare a scientific poster
    • write a well-structured scientific paper, chose an appropriate journal to submit the paper to, make revisions to the paper according the peer reviews
  2. Course content
    The student has to define a research issue, carry out research and write the thesis independently, although with support from a supervisor. At an early stage, a supervisor will be allotted to the student on the basis of their area of interest. Well before the actual period of the thesis work, a series of preparatory seminars will be held. There, students will present their ideas and plans for their research. It is the task of the supervisor to support the development from idea to plan, and thereafter to stimulate and criticize the student’s work.
    A thesis proposal/ research paper should consist of original, independently executed work. The general structure of the proposal’s content should be: (1) Specific aims; (2) Background and significance; (3) Preliminary studies; (4) Research design and methods. The general structure of the proposal’s content should be: (1) Introduction; (2) Data and Methods; (3) Results; (4) Summary and Discussion.
  3. Assessment
    Teaching takes place primarily through individual supervision and discussions in the student group at seminars, at different phases of the project, led by the examiner. Throughout the writing process, the student can consult her/his supervisor for advice, feedback and criticism.

Schedule

 

Week Course Content Lecturer Affilation
1. Nov. 2-5, 2021 Current Population Issues Introduction to Demography Albert Esteve Center for Demographic Studies (CED)
2. Nov. 8-13, 2021 Population data and science Dealing with data Tim Riffe Ikerbasque, Basque Foundation for Science
3. Nov. 15-19, 2021 Theory Historical Demography Lionel Kesztenbaum Institut National d’Études Démographiques (INED)
4. Nov. 22-26, 2021 Theory Mortality I Causes Jesus-Adrian Alvarez Interdisciplinary Centre on Population Dynamics (SDU)
5. Nov. 29-Dec. 3, 2021 Theory Mortality II Consequences Jesus-Adrian Alvarez Interdisciplinary Centre on Population Dynamics (SDU)
6. Dec. 13-17, 2021 Statistical Demography Causation Angelo Lorenti Max Planck Institute for Demographic Research (MPIDR)
7. Jan. 10-14, 2022 Current Population Issues Socio-economic indicators: Theory and applications Iñaki Permanyer Center for Demographic Studies (CED)
8, Jan.17-21, 2022 Current Population Issues Measuring the Generational Economy Bernhard Binder-Hammer Austrian Institute. Economics & Social Philosophy
9. Jan.24-28, 2022 Statistical Demography Event History Analysis 1 Jutta Gampe Max Planck Institute for Demographic Research (MPIDR)
10. Jan.21-Feb.4, 2022 Statistical Demography Event History Analysis 2 Jutta Gampe and Carlo G. Camarda Max Planck Institute for Demographic Research (MPIDR)

Institue National d’Études Démographiques (INED)

11. Feb.7-11, 2022 Statistical Demography Event History Analysis 3 Carlo G. Camarda Institut National d’Études Démographiques (INED)
12. Feb.14-18, 2022 Mathematical Demography Matrix Models and Stable Population Theory Hal Caswell University of Amsterdam
13. Feb.21-25, 2022 Population Data and Science Digital Demography Diego Alburez Max Planck Institute for Demographic Research (MPIDR)
14. Feb.28-March 5, 2022 Population Data and Science Accessing and Analyzing Web and Social Media Data Tom Theile Max Planck Institute for Demographic Research (MPIDR)
15.March 7-11, 2022 Theory Theories of Migration Clara Mulder University of Groningen
16. March 14-18,2022, 2021 Statistical Demography Agent-Based Modeling and Simulation Frans Willekens Netherlands Interdisciplinary Demographic Institute
17. March 21-25, 2022 Theory Migration Elena Ambrosetti Sapienza, Università di Roma
18. March 28-April 1, 2022 Theory Migration and Health Philippe Bocquier Université Catholique de Louvain
19. April 4-8, 2022 Statistical Demography Sequence Analysis Sergi Vidal Center for Demographic Studies (CED)
20. April 25-29, 2022 Statistical Demography Multilevel Data Analysis Konrad Turek Netherlands Interdisciplinary Demographic Institute (NIDI)
21. May 2-6, 2022 Theory Fertility, family, and the life-course Causes Brienna Perelli-Harris University of Southampton
22. May 9-13, 2022 Theory Fertility, family, and the life-course Consequences Daniele Vignoli University of Florence
23. May 16-20, 2022 Mathematical Demography Heterogeneous Populations – from discrete to continuous James W. Vaupel University of Southern Denmark
24. May 23-27, 2022 Mathematical Demography Decomposition Techniques José Manuel Aburto University of Oxford
25. May 30-June 3, 2022 Statistical Demography Qualitative Research in Demography Hinke Haisma, Billie de Haas, Stephen Adaawen University of Groningen
26. June 7-11, 2022 Modeling Simulation and Forecasting Population projections Ugofilippo Basellini Max Planck Institute for Demographic Research (MPIDR)
27. June 13-17, 2022 Mathematical Demography Alternative Measures Vladimir Canudas Romo ANU College of Arts and Social Sciences
28. June 20-24, 2022 Thesis courses Thesis work Mariona Lozano, Elisenda Rentería Center for Demographic Studies (CED)

How to apply

To be eligible for admission to the program, a student should hold a Master’s degree in demography, mathematics, statistics, public health, economics, sociology, geography, biology, computer science, history or another relevant field. Students enrolled in a Master’s degree can apply, but they are expected to have completed it and be in possession of the official title before the beginning of the EDSD program (in case of being accepted). At the time of application, students may, or may not be enrolled in a PhD program. Students participating in the EDSD without being enrolled in a PhD program are expected to enroll for such a program while doing the EDSD or immediately after.

Participants must have a curious mind and a deep interest in demography and population development either of humans or of species across the tree of life, and a demonstrable competence in English. EU citizens must have a valid European Health Insurance Card (EHIC) for the duration of the program. In case of being selected as a successful candidate, non-EU citizens will have to apply for student visa in the Spanish embassy in their country of origin.

Application deadline is April 15, 2021 for a September 1, 2021 start date. Successful candidates will be announced in June 2021.

To apply e-mail the following documents to edsd@ced.uab.es:

  • CV detailing educational and work history, language abilities, plus any scholarly publications.
  • One recent letter of recommendation.
  • Motivational letter (up to 5 double-spaced pages) introducing yourself and presenting your professional interests, background (beyond what is obvious from your CV) and plans. Explain how you expect EDSD could promote your future career. Include a detailed description of your quantitative skills (mathematics, statistics, and computer programming).
  • Official transcripts and degree certificates of your Bachelor’s and Master’s degrees in English or English translation.

TOEFL (internet-based: 100, computer-based: 250, paper-based 600) or CAE test scores. Alternatively, a degree taught in English or a declaration of why you are proficient in English.

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