ADVANCED MICROECONOMICS - 2027/8

Module Overview

Over the past few decades, a central focus of microeconomics has been understanding how organisations structure the allocation of knowledge, tasks, and earnings in the economy. Building on classical work in organisational economics and modern research on knowledge economies, this module examines how hierarchies, specialisation, and matching shape productivity, inequality, and economic outcomes.

Studying this module will help you apply and extend your knowledge of microeconomic theory by using organisational models to interpret real-world economic phenomena. You will engage with frontier research, work with economic data, and gain hands-on experience with Python and AI tools to implement simple quantitative applications, preparing you for research and data-driven roles in academia, policy, and industry.

Module provider

Economics

Module Leader

CARMONA Guilherme (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

Indicative content includes:

• Organisational economics: classical theories of firm size, hierarchies, and specialisation
• Knowledge-based hierarchies and the allocation of knowledge and tasks
• Organisational models of productivity, inequality, and economic outcomes
• Matching and organisational structure in labour and production markets
• Knowledge economies: theory and empirics
• Hands-on introduction to Python for economic data analysis
• Use of AI tools for coding, visualisation, and summarising research

Assessment pattern

Alternative Assessment

None

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of how organisations allocate knowledge, tasks, and earnings, their ability to apply organisational economics to interpret economic outcomes such as productivity and inequality, and their competence in using Python and AI tools for economic analysis.

The assessment strategy for this module has been revised as a result of the AI in education framework that the University has adopted to meet the challenges posed by generative AI to higher education. These changes strengthen the integrity of our assessment processes and safeguard critical thinking, and subject-specific and transferable skills development. The revised module is part of a structured and strategic approach to AI adoption at the University of Surrey which mitigates risks, creates consistency, and maximises opportunities to improve educational quality and prepare students for an AI¿driven future.

Thus, the summative assessment for this module consists of:

• Research proposal (25%)
  A written proposal, due in week 4, in which students identify a research question in organisational economics or knowledge economies, survey the relevant literature, and outline a suitable theoretical and/or empirical approach. This assesses the ability to engage critically with advanced scholarship and to design a feasible research project.
• Presentation (25%)
  An in¿class presentation in week 8, where students present their ongoing research and receive structured questions from peers and the lecturer. This assesses understanding of the theoretical framework, the ability to communicate complex ideas clearly, and skills in responding to critical feedback.
• Final project (50%)
  A written project, due in week 12, in which students apply organisational models and, where appropriate, economic data and Python/AI tools to analyse a specific problem related to knowledge allocation, productivity, inequality, or matching. This assesses the integration of theory, evidence, and computational tools, as well as independent research skills.

All summative assessments are explicitly aligned with the module learning outcomes and are designed to promote deep engagement with theory, critical analysis of economic phenomena, and the development of professional and transferable skills, including the responsible and effective use of AI tools.

Formative assessment and feedback

Students will receive formative feedback through:

• Lecture and practical¿session interaction, where questions and discussions provide immediate verbal feedback on understanding of theories, models, and empirical applications.
• Guidance on research ideas, including informal feedback on project topics and initial plans for the research proposal and final project.
• Opportunities to receive feedback on draft code and use of AI tools during the Python and AI components, helping students refine both their technical skills and their interpretation of outputs.

Module aims

• The aim of the module is to develop a comprehensive understanding of how organisations shape the allocation of knowledge, tasks, and earnings in the economy, to train students in using modern organisational economics to interpret economic outcomes such as productivity and inequality, and to equip them with hands-on skills in Python and AI tools that are relevant for research and for careers in data-driven roles in academia, policy institutions, and industry.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to develop your ability to use organisational economics to analyse how knowledge, tasks, and earnings are allocated in the economy, and to connect formal theory with quantitative applications using real and simulated data. It also aims to build practical skills in Python and AI tools for economic analysis and research.

The learning and teaching methods include:

• Lectures introducing core theories, models, and empirical applications in organisational economics and knowledge economies (recorded and disseminated as captured content).
• Practical sessions on Python and AI tools, focused on economic data handling, implementing simple numerical examples from the organisational models, and using AI tools for coding, visualisation, and summarising research papers.
• Student presentations and structured discussions of ongoing research projects, fostering critical engagement with the literature and peer feedback.
• Guided independent study, including preparation of a research proposal, a presentation, and a final project based on the core readings and additional materials (slides and code).

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECOM048

Other information

In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following area:

Employability

Through the use of real-world examples and problems related to organisational design, knowledge economies, and data-driven economic analysis, including hands-on experience with Python and AI tools that are highly valued in academia, policy institutions, and industry. 

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.