Master of Public Policy (MPP)

University of St Andrews

Causal Inference for Policy Analysis: provides foundational training in techniques for policy analysis on experimental and non-experimental data. The goal for students is to emerge from the module with a 'toolkit' of methods for estimating plausibly causal effects of policies.

Cost-Benefit Analysis: presents the fundamental methods of cost-benefit analysis, including understanding its strengths and limitations in application to policies.

Fundamentals for Policy Analysis: Coding, Mathematics, Statistics: prepares you for the term-time coursework in the MPP programme, introducing you to data analysis in Python, mathematics and statistics. The aim the module is to provide you with the tools you will need to complete the MPP successfully in a low-stakes environment. It will lay the groundwork for much of the subsequent coursework.

Microeconomics for Policy Analysis: introduces you to the basics of microeconomics with an emphasis on policy applications. The emphasis will be on case studies that illustrate the key theoretical points.

Policy Ethics: surveys topics relating to the ethics of policy and policy research, including human subjects research, professional ethics, ethics and economic methods, and fundamental questions such as distributive justice, democracy and the nature of expertise.

Policy-in-Practice: is devoted to the preliminary work you will do for your Policy-in-Practice project. In addition to addressing common issues like data access and missing data, the module will cover data manipulation and data visualisation.

Policy-in-Practice Project: is the culmination of the MPP when you will work in small groups to produce a policy analysis report for a domestic or international client under the direction of an academic staff member. Presentations of the reports will take place during the annual Policy Symposium in August.

Advanced Data Analysis: develops data analysis skills beyond those in Causal Inference for Policy Analysis. Potential topics include machine learning for classification, causal machine learning, robust inference, and unconventional data sources, for example, web scraping and text sources.

Survey Design and Analysis: covers the design, implementation and analysis of online, including using Qualtrics, and in-person surveys.

Behavioural Public Policy: examines how policies have been informed by the behavioural sciences through 'nudges', 'shoves', and 'budges'.

Education Policy: examines the role of education in society and the impact of education policies on students, parents, and the economy.

Empirical Analysis of Conflict and Terrorism: examines conflict and terrorism from an empirical perspective.

Health Policy: examines the relationship between health, healthcare and policies.

Immigration Policy: examines immigration policy and the impact of immigration on sending and destination countries.