29234 - international Demography

Learning outcomes

Upon successful completion of the course, students will have acquired an understanding of the complex and often problematic interactions between population dynamics and economic and social change.

The computer laboratory sessions will enable students to identify, access, and use demographic data from international databases with a good degree of autonomy.

By the end of the course, students will be able to:

• understand the main demographic patterns and trends across different world regions;
• place demographic behaviours within the broader context of social and economic development;
• critically discuss, from a comparative perspective, issues related to mortality, fertility, migration, and demographic development.
  

Course contents

The course explores the main topics in international demography, with particular emphasis on the relationships between population dynamics and economic and social development.

The course covers the following topics:

• Why study demography in a degree programme on Local and Global Development.
• International sources of demographic and socio-economic data for development studies; accessing, managing, and analysing demographic data.
• Basic methodological principles for constructing demographic and social measures.
• Theoretical approaches to the relationship between population growth and human development; convergence and divergence in demographic and social trends across world regions.
• Demographic, social, economic, and gender inequalities in the world’s population.
• Measures of inequality.
• Construction and analysis of development indicators.
• Health and survival inequalities.
• Gender inequalities in access to healthcare and wealth.
• Demography and conflict.
• Demography and globalization.
• Demographic trends and socio-economic change in Mediterranean countries.
• Demographic challenges in developing countries. Case studies: Latin America, Sub-Saharan Africa, China, and India.
• World population conferences; the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030).
• United Nations population projections.
• The role of international migration.

Readings/Bibliography

Part of the course material, particularly supplementary readings, will be provided by the instructor during the course.

Required readings

Course materials available on the Virtual Learning Environment.

Blangiardo, G.C. (2025). Elementi di demografia. Bologna: Il Mulino.

AISP (2025). Rapporto sulla popolazione. Verso una demografia positiva. Bologna: Il Mulino.

Rosina, A., & Impicciatore, R. (2022). Storia demografica d’Italia. Crescita, crisi e sfide. Rome: Carocci.

Billari, F. (2024). Domani è oggi. Costruire il futuro con le lenti della demografia.

Additional references

United Nations. Least Developed Countries (LDCs): Criteria and classification
https://www.un.org/development/desa/dpad/least-developed-country-category/ldc-criteria.html

United Nations (2024). World Population Prospects 2024
https://population.un.org/wpp/Publications/

Millennium Development Goals
United Nations Development Programme
http://www.undp.org/mdg/basics.shtml

Sustainable Development Goals
https://www.un.org/sustainabledevelopment/sustainable-development-goals/

Teaching methods

The course will be delivered entirely in person.

Teaching activities include:

• lectures;
• computer laboratory sessions on the management and analysis of demographic and social data;
• workshops on selected topics agreed upon during the course;
• active student participation through class discussions on previously assigned topics.

Assessment methods

Attending students

Assessment is based on an individual research project exploring in depth the demographic characteristics of a geographical area or a topic agreed upon with the instructor.

The project consists of:

• an oral presentation assisted by a PowerPoint presentation;
• a written report (maximum 10 pages).

Both must be submitted no later than one week before the presentation date. Detailed requirements will be discussed during the course.

During the oral presentation, students are expected to answer questions on both substantive and methodological aspects of their work.

Non-attending students

The assessment consists of:

• an oral examination covering the course contents;
• an individual research paper (maximum 10 pages).

The research topic must be agreed upon with the instructor (also by email) and submitted no later than one week before the examination date.

If the oral examination is not passed, the research paper will remain valid until the end of the official examination session for the academic year (February of the following year).

Use of Generative Artificial Intelligence (AI) in assessments

Generative AI may be used to support individual learning through additional explanations, summaries, and self-assessment activities.

For assessment purposes, only limited, declared, and non-substantive use of AI is permitted for support activities such as summarising texts, rephrasing content, or checking the correctness of programming code.

The substantial use of AI to complete any part of the assessment is not permitted.

Students with Specific Learning Disorders (SLD) or temporary/permanent disabilities

Students requiring accommodations are encouraged to contact the University’s Disability and Specific Learning Disorders Office as early as possible:

https://site.unibo.it/studenti-con-disabilita-e-dsa/en

The Office will propose appropriate accommodations, which must be submitted to the instructor for approval at least 15 days in advance. Approval will be granted where the proposed accommodations are compatible with the learning objectives of the course.

Grading scale

• 18–23: Satisfactory knowledge and analytical skills, although covering only a limited range of the course topics.

• 24–27: Good technical preparation with some limitations regarding the breadth of topics covered; good analytical skills, though not particularly well developed.

• 28–30: Excellent knowledge of a broad range of course topics, together with strong analytical and critical thinking skills.

• 30 cum laude: Outstanding, comprehensive, and in-depth knowledge of the course topics, combined with excellent critical analysis and the ability to make connections across different areas.

Students must register for examinations through the AlmaEsami online platform.

Teaching tools

Teaching activities will primarily make use of PowerPoint presentations, Microsoft Excel spreadsheets, and statistical software (e.g. Stata), depending on students’ prior knowledge and skills.

Additional learning resources will be provided throughout the course, including web resources, datasets, scientific articles, infographics, reports, data visualisations, indicator dashboards, interactive web applications, data and metadata repositories, opinion surveys, newspaper articles, and investigative journalism reports.

Office hours

See the website of Francesca Tosi

See the website of Rosella Rettaroli