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Public Policy Analysis: Data-Driven Governance and Policymaking


Admission requirements

Required course(s):

None, though the Politics of the Policy Process: Comparative Perspective is recommended.


This course is designed to teach students about the most important and controversial data, technologies, and processes currently being used to make decisions in the public sector. The course has a practical and utilitarian component, which is to introduce students to what these applications are. It also has an analytical component, which is to learn and apply critical thinking towards such applications using findings from scholarly literature and case studies.

On the first component, it will address questions of how the technologies work and why governments use them. For example, students will be introduced to the basic working principles of decision support systems, performance measurement, and smart public installations such as lighting and traffic systems. These are each different in important ways, and there are different technical and social consequences. Students will learn about key technology ‘affordances’ and frameworks to help them know what to highlight and be critical of.

A second component will involve learning about real world cases and the arguments and evidence available from academic research. This component will pose questions such as what ethical issues arise from use of the technologies, whether machines will decide all the important matters that affect our lives, and whether there is anything that governments and citizens can do to use technologies to make government better. For example, we may review and discuss cases of Google’s failed smart city project in Toronto, or the use of military-grade surveillance systems in domestic policing. The global spread of big data technologies will be used as an opportunity to learn to identify both local contextual factors and commonalities across cases.

Students will learn to navigate the complex technical and ethical issues and put forward their own arguments through discussion and debate. Together, these skills and knowledge will help students to critically assess these tools and to become more expert both as critical users of technologies and as systems architects.

Course Objectives

  • Describe examples of different kinds of data-driven applications in public services

  • Debate the public values pros and cons of data-driven decision making in the public sector using real world examples and academic literature

  • Carry out a research project that critically evaluates an algorithmic decision-making system from the perspective of protecting and improving public values.


Timetables for courses offered at Leiden University College in 2022-2023 will be published on this page of the e-Prospectus.

Mode of instruction

The class will consist of both lecture and case-based seminar/workgroup formats; for each week the second class meeting will use a case study to elucidate the material covered in the previous lecture.

Assessment Method

  • Class participation (individual)

  • Policy memo (individual)

  • Analysis briefing (indvidual)

  • Research report (group)

Reading list

Will be announced in the course syllabus.


Courses offered at Leiden University College (LUC) are usually only open to LUC students and LUC exchange students. Leiden University students who participate in one of the university’s Honours tracks or programmes may register for one LUC course, if availability permits. Registration is coordinated by the Education Coordinator,


Dr. Matthew M. Young,