COURSE DESCRIPTION

The course addresses key aspects of regional development, drawing on theoretical and practical perspectives relevant to the European Union (EU). Through a thorough analysis of economic processes and regional policies, the course aims to provide students with a deep understanding of the dynamics of regional economies, equipping them to critically assess and shape sustainable and inclusive development strategies.

Detailed course description is available here.

COURSE LECTURER

Assistant Professor Vinko Muštra, PhD (Lecturer CV and information is available here.)

Assistant Professor Blanka Šimundić, PhD (Lecturer CV and information is available here.)

LEARNING OUTCOMES

• Evaluation of various economic processes from the perspective of regional development and understanding of relevant regional policies.
• Identify and understand the key elements of basic concepts of regional growth in the EU.
• Analyze the advantages and disadvantages of the most relevant theories of interregional trade and regional migration.
• Understand and compare the key factors of regional growth in the EU, with an emphasis on the innovation ecosystem and governance quality.
• Critically assess regional policies and the trade-off between equity and efficiency, drawing on analyses within the EU.

TARGET AUDIENCE

• Graduate students in economics (or in social sciences, technical sciences or science related fields) who intend to round out their knowledge in the field and use it in the proces of preparing dissertations.
• Holders of undergraduate degrees or students in the last year of ther studies in economics or the social sciences who wish to study the field of behavioural economics.
• Researchers and professionals working in the field.

CREDITS TRANSFERS (ECTS)

This FEBT Summer school program offers participants the possibility of going through evaluation process for the purspose of requesting official credit transfers (ECTS).

• Evaluation process

Participants who wish to acquire 6 ECTS will be evaluated based on the regular and active course attendance and taking two mid-term exams and writing a final paper assignment (the project task is individual or group work which implies selection of the suitable machine learning method for a particular problem, analysis and comparison of the results and selection of the optimal model).

CERTIFICATE OF ATTENDANCE

Participants not interested in credit transfer will instead receive the Certificate of Attendence, stating the course completed. These students will be neither evaluated nor graded.