Bachelor of Arts (BAEcon)

BAEcon Development Studies

In-depth study into the problems and options faced by the developing world.

  • Duration: 3 or 4 years
  • Year of entry: 2025
  • UCAS course code: L900 / Institution code: M20
  • Key features:
  • Study abroad
  • Industrial experience

Full entry requirementsHow to apply

Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £29,500 per annum. For general information please see the undergraduate finance pages.

Policy on additional costs

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).

Scholarships/sponsorships

Scholarships and bursaries, including the Manchester Bursary , are available to eligible home/EU students.

Some undergraduate UK students will receive bursaries of up to £2,000 per year, in addition to the government package of maintenance grants.

You can get information and advice on student finance to help you manage your money.

Course unit details:
Quantitative Methods

Course unit fact file
Unit code ECON20222
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Available as a free choice unit? Yes

Overview

The general purpose of this course is to provide students with a non-technical introduction to the basic methods of econometrics. The focus will be on:

1. enabling students to perform basic data handling (uploading, re-categorising, cleaning) in a statistical software and to implement the statistical techniques taught in the course in that software;

2. giving students a basic understanding and working knowledge of multivariate regression;

3. developing students' understanding of the difficulties associated with drawing causal inference;

4. developing students' understanding of popular techniques of establishing causal relationships;

5. understanding issues arising from non-stationary time-series and gain a beginning understanding of time-series modelling and forecasting

6. enabling students to read, understand and critically assess published empirical research using the techniques taught in the unit

 

At the end of this course students should be able to:

• Understand how the use of statistics and econometrics can inform substantive discussion

• Have obtained a firm understanding of summary statistics

• Understand the basic tenants of regression analysis

• Understand issues arising from non-stationary time-series

• Understand the difficulties in establishing causal relationships

• Understand popular techniques of establishing causal relationships

• Be able to handle complex dataset and perform basic data-cleaning tasks

• Be able to perform statistical analysis in a software package (R)

• Read, understand and critically assess published empirical work

• Contribute productively to a substantive piece of empirical work as a member of a group 

Pre/co-requisites

Unit title Unit code Requirement type Description
Introductory Mathematics ECON10061 Pre-Requisite Compulsory
Introductory Statistics for Economists SOST10062 Pre-Requisite Compulsory
Advanced Mathematics ECON10071A Pre-Requisite Compulsory
Advanced Statistics ECON10072A Pre-Requisite Compulsory
Advanced Mathematics ECON10071B Pre-Requisite Compulsory
Advanced Statistics ECON10072B Pre-Requisite Compulsory
ECON20222 Prerequisites: (ECON10071A AND ECON10072A) OR (ECON10071B AND ECON10072B) OR (ECON10061 AND SOST10062)

(ECON10071 and ECON10072) or (ECON10061 and SOST10062)

Aims

The general purpose of this course is to provide students with a non-technical introduction to the basic methods of econometrics. The focus will be on:

1. enabling students to perform basic data handling (uploading, re-categorising, cleaning) in a statistical software and to implement the statistical techniques taught in the course in that software;

2. giving students a basic understanding and working knowledge of multivariate regression;

3. developing students' understanding of the difficulties associated with drawing causal inference;

4. developing students' understanding of popular techniques of establishing causal relationships;

5. understanding issues arising from non-stationary time-series and gain a beginning understanding of time-series modelling and forecasting

6. enabling students to read, understand and critically assess published empirical research using the techniques taught in the unit

Syllabus

The exact syllabus may vary and the following is indicative

An Introduction to R and RStudio and Summary Statistics

Understanding the basic workings of R. Importing data. Basic data operations including summary statistics. How to fix problems. Slicing data in different dimensions and conditional summary statistics.

Multivariate Regression Analysis and Inference - and Applications in R

Assumptions and resulting properties of multivariate regression analysis. Omitted variable bias. Basic inference problem. Test statistics and their distribution under the null hypothesis. P-values. Assumptions and robust inference (as standard!). t-tests and F- tests.

Causal relationships - Selection issues and applications of regression models

An introduction to issues arising when attempting to establish a causal relationship.

Introduce selection problems (on observables and un-observables). Understanding that regression helps when selection is on observables. Selection on unobservable as the source of difficulties.

Panel data and Difference in Difference - and Applications in R

Cross-Section or Panel structure of data. How to use Panel data to remove time-invariant unobservables.

Correlation, Causation and non-Stationarity in Time-Series Data

Understanding the characteristics of non-stationary data. Spurious regressions.

Economic forecasting - Understanding the pitfalls

Use Surveys of Professional forecasters to understand variation in forecasts and

uncertainty embodied in forecasts. Use AR(1) model to produce basic forecasts. Basic tenants of forecast evaluation. Evaluating forecasts for binary outcomes.

Teaching and learning methods

Synchronous activities (such as Lectures or Review and Q&A sessions, and tutorials), and guided self-study

Knowledge and understanding

At the end of this course students should be able to:

• Understand how the use of statistics and econometrics can inform substantive discussion

• Have obtained a firm understanding of summary statistics

• Understand the basic tenants of regression analysis

• Understand issues arising from non-stationary time-series

• Understand the difficulties in establishing causal relationships

• Understand popular techniques of establishing causal relationships

• Be able to handle complex dataset and perform basic data-cleaning tasks

• Be able to perform statistical analysis in a software package (R)

• Read, understand and critically assess published empirical work

Intellectual skills

(i) problem-solving skills; (ii) ability to analyse and interpret empirical data; (iii) the

evaluation and critical analysis of arguments, theories and policies; (iv) synthesise and evaluate data.

Practical skills

(i) ability to analyse and interpret empirical data; (ii) good working knowledge of statistical software.

Transferable skills and personal qualities

(i) select and deploy relevant information; (ii) communicate ideas and arguments in writing and verbally; (iii) apply skills of analysis and interpretation; (iv) manage time and work to deadlines; (v) use ICT to locate, analyse, organise and communicate information (e.g. internet, on-line databases, search engines, library catalogues, spreadsheets, specialist programs, word processing and presentation software) (vi) ability to work in a small group.

Assessment methods

  • 10% in-term assessment (R skills, online test)
  • 40% group project (2000 words)
  • 50% final exam

Feedback methods

Online quizzes, Practice questions in computer and exercise classes, office hours, discussion board

Recommended reading

Joshua D. Angrist & Jörn-Steffen Pischke (2014) Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press

Teaching staff

Staff member Role
Ralf Becker Unit coordinator

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