99612 - Local Development Demography

Learning outcomes

The laboratory aims to provide expertise on techniques for demographic analysis at the local level. At the end of the laboratory, the student is able to analyze demographic data, understand the dynamic and structural components that determine demographic change and estimate the expected evolution of the population.

Course contents

The laboratory introduces students to the use of the main local and national demographic databases and to the analytical techniques required to interpret and project population change.

Through hands-on activities, students will become familiar with the methods used to develop demographic projections, examining the contribution of fertility, mortality, and migration to population dynamics and discussing the implications of demographic change for the social, economic, and territorial development of communities.

Readings/Bibliography

Required readings

• Blangiardo G.C. (2006). Elementi di demografia. Bologna: Il Mulino.
• De Rose A., Rosina A. (2022). Introduzione alla demografia. Analisi e interpretazione delle dinamiche di popolazione. Milan: Egea.

Teaching methods

The laboratory combines short introductory lectures with hands-on practical sessions using Stata.

Activities focus on the application of demographic methods and techniques through the analysis of real demographic data and case studies.

Attendance is compulsory.

Students are required to attend at least 80% of the laboratory sessions. Since the laboratory consists of 20 hours, students may miss no more than 4 hours (equivalent to two sessions).

Assessment methods

Assessment is based on the development and discussion of a case study carried out during the laboratory.

The final mark (out of 30) will take into account the methodological soundness of the analysis, the ability to manage and interpret demographic data, the discussion of the results, and the overall quality of the presentation.

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 materials, including datasets, exercises, lecture slides, and additional resources, will be made available through the University’s Virtuale platform.

Office hours

See the website of Francesca Tosi

See the website of Rosella Rettaroli