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ECON50730

Academic Year 2025/2026

PhD Macroeconomics 2 (ECON50730)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
5 (Doctoral)
Credits:
10
Module Coordinator:
Dr Alexander Yarkin
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This PhD Macro-2 module covers theories and empirics of Economic Growth, as well as a collection of topics in adjacent areas, such as Globalization and Growth, Political Economy of Development, History and Path Dependence, and so on. The module has two parts.

The first part covers core models and empirical approaches to investigating the mechanics of Economic Growth. It presents classical models of growth, such as the Solow model, Endogenous, Schumpeterian, and Unified Growth theories. It also covers key empirical papers (and associated methods) that test some of the main predictions of these growth theories.

The second part covers several topics at the intersection of Growth and adjacent areas. It mainly focuses on empirical papers and follows a seminar-style course format. This part covers the following topics: (i) Political Economy of Development, (ii) Culture and Institutions, (iii) Inequality and Growth, (iv) Globalization and Growth, (v) History and Path Dependence, (vi) Role of Geography.

About this Module

Learning Outcomes:

The learning outcomes of the module are (I) developing an understanding and ability to apply key theoretical and empirical methods in the study of Long-Run Growth and its determinants; (II) the ability to understand and critically evaluate recent academic papers and advancements in economic growth; (III) the ability to generate research ideas and conduct research in associated topics.

Student Effort Hours:
Student Effort Type Hours
Lectures

25
Autonomous Student Learning

200
Total

225

Approaches to Teaching and Learning:
Lectures, enquiry & problem-based learning.

Policy on the use of Generative AI: Generative AI, such as ChatGPT, may be used in this module in the following ways:

• To understand main concepts/theories and find definitions.
• To correct grammar and improve the writing style of your own work.
• For technical assistance, such as instructions on how to use a statistical software.

Please note the following:
1. The use of AI-generated content without explicit permission and attribution is a form of academic misconduct. Plagiarism risk is high when using AI, and it is not acceptable to submit AI-generated content as it is. If you copy and paste entire AI-generated answers, it will be considered plagiarism. Even AI co-created work is considered collusion if you present it all as your own.

2. You are therefore required to explicitly state whether AI was used in the assignment and how.

3. You should also check all information provided by AI for accuracy. Be sure that you can verify all generated text, again, do NOT simply copy/paste the AI-generated information into your assignments.

4. If academic misconduct is suspected, you may be asked to discuss or explain (verbally and without prior notice) components of your assignment to determine the authenticity of the work.

Requirements, Exclusions and Recommendations
Learning Requirements:

No requirements


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Midterm exam: several problems on theories and empirics of Economic Growth Week 6 Alternative linear conversion grade scale 40% No
30
 No
Assignment(Including Essay): Problem set on growth theories and empirics. Week 4 Alternative linear conversion grade scale 40% No
10
 No
Report(s): Mock referee report and presentation of a chosen paper in the 2nd part of the course Week 9 Alternative linear conversion grade scale 40% No
10
 No
Assignment(Including Essay): Final research paper submission Week 15 Alternative linear conversion grade scale 40% Yes
25
 Yes
Exam (In-person): Final exam in the form of presentation of your preliminary research paper developed during this course. Week 14 Alternative linear conversion grade scale 40% Yes
25
 Yes

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 16:00 - 17:50
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