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Studies

European Doctoral School of Demography

ABOUT

Cabecera

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. The School’s courses are structured in such a way that the students work on the precise formulation of a thesis topic and early steps of their dissertations. 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 2020/21 academic year, the School will be held at two different locations. The preparatory courses (from early September to late October 2020) will be offered at the Max Planck Institute for Demographic Research in Rostock, Germany. The core courses (from November 2020 until end of July 2021) will be held at the Centre for Demographic Studies (Centre d’Estudis Demogràfics – CED) at the Universitat Autònoma de Barcelona, Spain.

Scholarships and Travel Grants

The program provides scholarships and travel grants for all final selected candidates, sponsored by the Institutions within the EDSD consortium. The scholarship includes a monthly stipend for the 11 months of the program, and it also covers travel expenses associated with the program.

Master’s Degree in Demography

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

COURSES

Preparatory courses in Rostock

The courses in Rostock consists 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 six courses on (1) Mathematical Demography, (2) Statistical Demography , (3) Demographic Theories , (4) Population Data and Science, (5) Demographic Modeling, Simulation, and Forecasting and (6) Thesis courses. In addition, computer, graphic and presentation skills are taught. Students devote 20% of their time to thesis research.

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

Check the current schedule at CED-Barcelona in the right-click menu.

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 covers various advanced topics in formal demography:

    • Mathematical Theory of Population  Heterogeneity
    • Growth and Projection of Populations and  Cohorts
    • Agent-based Modeling and Simulation
    • Stable Population, Nonstable Populations, Population Momentum
  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.


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 will deal with so called event history models. These are statistical techniques to analyze the occurrence of events in time, such as death, marriage, childbirth, entry into retirement etc. We will cover the following topics:

    • 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
  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 migrationmake 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 course, students should comprehend the major theories that explain the level and timing 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 suing information with details at individual and family, and household levels
    The course is divided into four modules:

    • Theories of fertility and the family
      • Major trends in fertility and the family
      • Natural fertility and proximate determinants of fertility
      • Demographic strategies and demographic transitions around the globe
      • 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 intergenerational transfers of values, norms and resources)
    • Theories of mortality and morbidity
      • Major trends in life expectancy, health expectancy, diseases and causes of death
      • The biodemography of aging (including evolutionary-demographic theories and biodemographic diversity of mortality patterns)
      • Late life legacy of very early life
      • Theories of the epidemiologic transition.
      • 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).
    • Migration
      • Main theoretical approaches of migration and residential mobility, derived from various disciplines: demography, geography, economics, social psychology
      • The implications of the theories for empirical research.
    • Historical demography
      • Population and economy. The recent debate and its intellectual sources
      • Population and economy. Long term macro evidence.
      • The demographic transition theory revised: the mortality decline
      • The demographic transition theory revised: the fertility decline
      • Short-term economic stress. Macro evidence.
      • Population and living standards. From macro to micro.
      • The demographic transition and population ageing.
  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 divides in three modules:

    • Introduction to Demography. Data quality and types: This module deals with sources of demographic data such as censuses and registers, illustrating the importance of administrative records in secondary analysis. The module then moves on to demographic and social surveys, from questionnaire construction, through data reliability issues, and on to analysis. The module concludes with a discussion of data comparability and the harmonization of various data sources.
    • Dealing with Data
      The most often used basic summary measures are described, as well as their strengths and shortcomings. The implicit hypotheses behind the calculations are made explicit. The following concepts and tools are presented and discussed: crude rates, age-specific rates, summary indices based on rates of the 1st kind or the 2nd kind, net and gross probabilities, population change during one year and reproduction from one generation to the next, life table indexes, multiple-decrement life table, multistate life tables, methods of standardization, heterogeneity of populations, period and cohort summary indexes, compositional and tempo effects in period measures.
    • 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 in new ways.
      The main goal of the course is to advance fundamental population science by addressing key questions in demography from the perspective of the digitalization of life.
      This includes:

      • Complementing digital trace data, innovative forms of data collection and traditional sources to improve our understanding of demographic processes
      • Evaluating the socio-demographic implications of the digital revolution
      • Leveraging statistical, mathematical and computationally-intensive approaches to generate new insights into population dynamics
  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. Modelling, Simulation and Forecasting

  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

    • Modelling and Simulation
      Students get acquainted with the matrix notation, and learn about modelling and simulation of nonlinear-interacting populations. They have to program population projections in R and learn about the stable population model through numerical simulations. The program ”Populus” is used to study the dynamics of interacting populations and in particular to study models of disease infection and immunization programs.
    • Population Forecasting
      This is a “hands-on” module in which the students carry out, in groups of 2-3, a forecast of a chosen country, region, or sub-population. In the process the students learn to acquire jump- off data for the population of interest, and if necessary, to adjust them for undercount and to update the values to the most recent realistic jump-off time. The students learn how to generate scenarios for fertility and mortality using Brass. Relational model and Gamma fertility model as an example. They learn how to use these models in the framework of cohort-component population projections.
      Based on population projection and using extension of the headship rates method, students should be able to implement household projection for the chosen population.
      Students learn about new, recently introduced indicators of aging and to apply them to observed and projected populations.
  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. 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 her/his area of interest. Well before the actual period of the thesis work a series of preparatory seminars will be held, where the students present ideas and plans for the 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 an 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. 4-7, 2019 Population Data and Science Introduction to Demography. Data quality and types Albert Esteve Center for Demographic Studies (CED)
2. Nov. 11-14, 2019 Population Data and Science Dealing with Data Tim Riffe Max Planck Institute for Demographic Research (MPIDR)
3. Nov. 18-21, 2019 Theory Historical Demography Lionel Kesztenbaum Institut National d’Études Démographiques (INED)
4. Nov. 25-28, 2019 Theory Fertility, family and the life course (causes) Daniele Vignoli University of Florence
5. Dec. 2-5, 2019 Theory Fertility, family and the life course (consequences) Brienna Perelli-Harris University of Southampton
6. Dec. 9-13, 2019 Statistical Demography Causation Maarten Bijlsma  Max Planck Institute for Demographic Research (MPIDR)
7. Dec. 16-20, 2019 Statistical Demography Event History Analysis 1 Jutta Gampe  Max Planck Institute for Demographic Research (MPIDR)
8. Jan. 13-17, 2020 Statistical Demography Event History Analysis 2 Carlo G. Camarda Institut National d’Études Démographiques (INED)
9. Jan. 20-24, 2020 Statistical Demography Event History Analysis 3 Carlo G. Camarda Institut National d’Études Démographiques (INED)
10. Jan. 27-31, 2020 Statistical Demography Event History Analysis 4 Jutta Gampe Max Planck Institute for Demographic Research (MPIDR)
11. Feb. 3-7, 2020 Current Population Issues Demography and Inequality (Theory and Research) Diederik Boertien Center for Demographic Studies (CED)
12. Feb. 10-13, 2020 Statistical Demography Agent-Based modeling and simulation Frans Willekens Netherlands Interdisciplinary Demographic Institute
13. Feb. 17-21, 2020 Theory Migration Elena Ambrosetti Sapienza, Università di Roma
14. Feb. 24-28, 2020 Theory Migration and Health Philippe Bocquier Université Catholique de Louvain
15. March 2-6, 2020 Theory Theories of Migration Clara Mulder University of Groningen
16. March 9-13, 2020 Mathematical Demography Stable Populations Hal Caswell University of Amsterdam
17. March 16-20, 2020 Theory Mortality 1 (causes) Virginia Zarulli Interdisciplinary Centre on Population Dynamics (SDU)
18. March 23-27, 2020 Theory How conditions in early life affect outcomes in later life Tommy Bengtsson Lund University
19. March 30 to April 3, 2020 Population Data and Science Digital Demography Diego Alburez Max Planck Institute for Demographic Research (MPIDR)
20. April 14 to April 16, 2020 Statistical Demography Sequence Analysis Sergi Vidal Center for Demographic Studies
21. April 20-24, 2020 Current Population Issues Measuring the generational economy Bernhard Hammer Vienna Institute of Demographie (VID)
22. April 27-30, 2020 Mathematical Demography Decomposition techniques in health research José Manuel Aburto University of Southern Denmark (SDU)
23. May 4-8, 2020 Statistical Demography Multilevel Data Analysis Konrad Turek Netherlands Interdisciplinary Demographic Institute (NIDI)
24. May 11-14, 2020 Theory Mortality 2 (Consequences) James W. Vaupel University of Southern Denmark
25. May 18-21, 2020 Mathematical Demography Heterogeneous Populations – from discrete to continuous James W. Vaupel University of Southern Denmark
26. May 25-29, 2020 Modeling, Simulation and Forecasting Age-period-cohort Models Bendix Carstensen Steno Diabetes Center Copenhagen
27. June 1-5, 2020 Modeling, Simulation and Forecasting Population projections and forecasts Ugofilippo Basellini Max Planck Institute for Demographic Research (MPIDR)
28. June 8-12, 2020 Modeling, Simulation and Forecasting Demographic forecasting Heather Booth ANU College of Arts and Social Sciences
29. June 15-19, 2020 Mathematical Demography Alternative measures Vladimir Canudas Romo ANU College of Arts and Social Sciences
30. June 29- July 3, 2020 Thesis work
31. July 6-10, 2020 Thesis work
32. July 13-18, 2020 Thesis work
33. July 20-24, 2020 Final Presentations

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, 2020 for start date in Sept 2020. Successful candidates will be announced in June 2020. 

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.