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You are here Postgraduate > Diploma in Applied Economics and Big Data > Course Structure > Impact Evaluation and Big Data

Impact Evaluation and Big Data

Module Code: ECP77473

  • ECTS Credit: 5
  • Mandatory/Optional: Optional
  • Semester/Term Taught: Michaelmas Term
  • Module Coordinator: Professor Carol Newman

Aims of Module

What is impact evaluation? How can we neatly identify the causal impact of a certain program or policy or event on the outcomes of interest? Which tools do we have at disposal? This module will address these questions, by focusing on the selection problem that typically arises in impact evaluation studies. We will discuss ways in which properly designed studies can address it. We will dedicate the first part of the module to discuss the design of sound experiments (randomized controlled trials). We will then discuss alternative solutions, for settings in which experiments are not feasible and/or desirable. Although the tools studied in this module have a broad application, examples and case studies will be taken mostly from the development economics literature.

Module Delivery

The module will be delivered through a combination of lectures and tutorials. Problem sets will be circulated throughout the module and answers are to be submitted before the tutorials, at which they are discussed.

Learning Outcomes

The module will partly build upon the econometric module from the first term, although the focus is going to be more on the application of the tools. On completion of the module students should be able to use a variety of tools to design and run a rigorous impact evaluation studies. Students will be familiar with set of tools available to address selection problems, and will have a full understanding of their application, both from a theoretical and a practical point of view.

Syllabus

Topics covered in this module include:

  • Impact Evaluation and the Selection Problem;
  • Randomized Controlled Trials;
  • Non-Experimental approaches: Difference-in-Difference; instrument variable; Regression Discontinuity

Assessment

Assessment for the module is based on Continuous Assessment (50%) and a Project (50%)

Module Website

Blackboard