MSc Development Finance

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £15,200
  International, including EU, students (per annum): £31,500

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We know that student finance can be complicated. The links below provide further information to help guide you.

• Learn more about - student finance options for UK students .
• Learn more about - fees and finance for international students .
• Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.

The course introduces students to the challenges that present themselves when analysing real-world data and equips students with the necessary skills to conduct independent good-quality empirical work relevant to research questions in development studies and development economics. The course is relevant for students in these disciplines who want to gain a better understanding of published empirical work and gain skills in data analysis themselves.

Students must have prior knowledge of advanced statistics to follow this course. If you select ‘Econometric Methods for Development’ [MGDI60031] as an optional course, you must obtain approval from the course lead within the first week of the semester.

Attendance of a two-week pre-sessional ‘Introduction to Quantitative Methods’ course is mandatory. This pre-sessional course will revise the quantitative skills that are necessary to engage fully with the subsequent econometrics teaching. Teaching on ‘Econometric Methods for Development’ will start in week two of semester one

Feedback Prior Knowledge in advanced statistics is required. 

The course aims to:

• Endow students with an understanding of the challenges that present themselves when analysing data.
• Enable students to interpret and critically evaluate empirical research outputs published in leading applied economics and development economics journals.
• Provide students with practical skills in testing, modelling, and evaluating theories and economic relationships using different types of data obtained from actual data sets.
• Equip students with the knowledge and skills necessary to carrying out independent good quality empirical work demanded of an academic researcher or practitioner in development economics.
• Allow students to implement a battery of techniques required to estimate micro and macro relationships specific to development and development economics, using the econometric software package R.

The course requires students to take the pre-sessional Course – Introduction to Quantitative Methods in welcome week and week 1.

The course is built around the following topics stretching over 10 weeks of teaching:

Introduction to Data Analysis for Development

Bivariate Regression: Diagnostics and Specification Tests

Multivariate Regression: Interpretation and Model Choice

Time-series Regression dealing with Non-Stationarity of Variables

Model Testing and Evaluation
 

The course adopts hands-on and blended learning techniques, using a mix of lectures, hands-on workshops, small group tutorials, and some pre-recorded teaching material. Emphasis is put on hands-on data analysis, research design, model choice and interpretation of regression outputs. In line with its objectives, the teaching in this course will draw on exercises using real-world secondary data and specialist code-based statistical software. Theoretical concepts are introduced during weekly one-hour lectures.

Mathematical proofs are kept at a minimum and lectures will focus on core theories and concepts instead. Students are encouraged to read relevant textbook chapters to cover mathematical proofs as background readings to support their understanding of theoretical concepts in preparation for the lecture.

Data analysis and software applications are introduced in weekly one-hour workshops. The workshop sessions are accompanied by an annotated log of the codes required to replicate results. Students are asked to replicate software applications in preparation for the workshops. The workshop sessions focus on joint discussion and interpretation of results, linking to the theoretical material covered during lectures.

Students understanding of and skills in data analysis are further supported through exercises which students are asked to prepare for weekly tutorials. Exercises draw on real-world secondary data and economic applications.

Students are required to complete the pre-sessional part of the course, which is assessed online. The mark does not count towards the overall grade of this course. However, submission is mandatory as this early assessment element provides an indication of gaps in students' knowledge which will be addressed in the early weeks of the course.

Weekly exercises, which are formative non-graded assessment elements, are complemented by two summative assessment elements. A 1,000-word research report which accounts for 40 per cent of the overall grade and an open book take home exam which accounts for 60 per cent of the overall grade. The research report presents analytical results for a given problem set. The report submissions are accompanied by the submission of relevant code and data files. The take home exam consists of a set of questions and exercises which students must complete within a 24-hour turnaround time.

• identify features and characteristics of different type of data and empirically evaluate these features.
• explain core concepts and techniques in data analysis and econometrics in the context of development.
• gauge the importance of econometrics results in the design of development policies.

• empirically evaluate economic theories and models, related to development, by use of actual data sets.
• critically evaluate applied work in the fields of development economics and development.
• understand advantages and limitations of each econometric method and their applications in development economics and development.

• estimate and interpret different models and identify how they relate to each other.
• conduct different residual and model diagnostic tests and conclude on the adequacy of model choice against data evidence.
• identify suitable econometric technique(s) relevant to research questions in the fields of development economics and development.

• Conduct applied independent research using code-based econometric and statistical software such as R.
• Concisely summarise empirical results and compile research reports.
• Be able to assist in the design of policy by proving quantitative evidence produced by drawing on the tools introduced in this module.

S1. Research report (with annotated code) Students will be allocated in groups of 2-3 and work on the code together. After completion of the code, students must write up and submit the research report individually.

S2. Take-home exam

Feedback on code will be provided during tutorials before submission. Feedback on the report will be provided some time after the submission deadline (week 9).

Feedback on the exam will be provided some time after the submission deadline (during the exam period).

Primary readings for the course are the following two textbooks:

Söderbom, M., F. Teal, M. Eberhardt, S. Quinn, A. Zeitlin (2015) Empirical Development Economics, Routledge, ISBN: 9780415810494. https://www.empiricalde.com/

Wooldridge J M (2016) Introductory Econometrics: A Modern Approach, 6th edition (or earlier). Cengage Learning, ISBN: 9781305270107. 
The following textbooks are useful additional references:

Asteriou D and S G Hall (2011) Applied Econometrics, 2nd edition, Palgrave Macmillan, ISBN: 9780230271821. 
Banerjee, A, JJ Dolado, JW Galbraith, D Hendry (1993) Co-integration, Error Correction, and the Econometric Analysis of Non-stationary Data, Oxford University Press, ISBN: 9780198288107.

Heiss, F (2020) Using R for Introductory Econometrics, 2nd edition (or earlier). ISBN: 9798648424364.

Freely available here: http://www.urfie.net/index.html 
All students are asked to go through RStudio Basics in preparation for this course: https://posit.cloud/learn/recipes

Additional readings on specialised topics and empirical case studies are made available each week via the online learning platform

Available as free choice:

• MSc Development Economics and Policy
• MSc Development Finance
• MSc Public Policy and Management
•  

Available as free choice with appropriate background (students need to obtain approval from the course lead within the first week of the semester):

• MSc International development Pathways
• All other University of Manchester PGTs