MSc Development Finance / Course details

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 is divided into parts:  

Part I: Pre-sessional (during Induction week)

• Revisions of basic statistical tools for Development
• Introduction to R/RStudio

Part II: Main sessions-they are 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.

• 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