• Identify policy interventions across different areas resting on insights drawn from evidence.
• Relate different concepts of evidence, data, and expertise to policy design and evaluation.
• Analyze and evaluate key ways in which stakeholders provide evidence for politicians.
• Assess the role of different methods and practices suitable for identifying selecting and incorporating evidence in the policy making process.
• Critically discuss the ethical and transparency related challenges posed by data and artificial intelligence in policy design and public administration.
• Develop coherent arguments building on evidence based insights with implications for policy design and delivery.

The course discusses the role of evidence---e.g., data, expertise, scientific knowledge---in the design and evaluation of public policies. It focuses on how politicians gather such data, who supplies evidence and why, and how this input is processed to evaluate the success or failure of various public policies. The course comes at the time of increased reliance of data, AI, and digital transformation that shape public policy and public management. This is not a technical course; rather it reviews issues of evidence quality, transparency, legitimacy, and common approaches in different policy areas that incorporate data and evidence to fundamentally alter how decisions are taken at a much faster pace and almost micro-focus. We will work through policy area specific case studies and applications, such as tax compliance, health policy, reduction of poverty, green transition and so on. The course directly connects to Applied Policy Analysis and the other stream course.

In relation to Nordic Nine

This course focuses on understanding the use of public policy relevant data and evidence. The course equips students with a plethora of transformative capabilities in accordance with the Nordic Nine. The concentration on evidence based policy alternatives requires a data driven framework and the success or failure of these policies requires systematic incorporation and interpretation of ambiguity (NN2). In conjunction with this capability, the course relies heavily on previous methodological training received by students and forces them, to re-learn those methods, apply them in specific areas, and discuss these with others (NN8). These areas also present serious ethical challenges pertaining to transparency, long- and short term benefits, or potential inequalities or back-fire effects, presenting students with fundamental ethical dilemmas (NN5). Furthermore, student capabilities regarding the production of prosperity and protection of the prosperity of next generations (NN7) are fostered through sections of the course that are dedicated to green policies, but also issues of inequality and poverty. In tandem, these core aspects can enhance policy design or compliance and ultimately safeguard responsible policy making and promote temporally responsible choices. The course fosters in-class group work and relies on peer-discussions in class, but also expects students to prepare at least one individual final paper that engages critically with a topic provided. Overall, the course creates an environment conducive of being "you are critical when thinking and constructive when collaborating" (NN6). Finally, given that evidence based policies create the context in which businesses operate and also rely on input from businesses, the course indirectly relates to NN1 and NN3.
This course is closely related to: NN2, NN5, NN6, NN7, NN8
This course partially speaks to:   NN1, NN3

Sample readings:

Bogenschneider, K., & Corbett, T. (2021). Evidence-based policymaking: envisioning a new era of theory, research, and practice. Routledge.

van Ooijen, C., Ubaldi, B., & Welby, B. (2019). A data-driven public sector: Enabling the strategic use of data for productive, inclusive and trustworthy governance. OECD Working Paper.

Bunea, A., & Chrisp, J. (2023). Reconciling participatory and evidence-based policymaking in the EU Better Regulation policy: mission (im) possible?. Journal of European Integration, 45(5), 729-750.

Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI–A transformational shift for government: So, what next for research?. Public Policy and Administration, 35(1), 24-44.

Callander, S. (2008). A theory of policy expertise. Quarterly Journal of Political Science, 3(2), 123-140.

Mertens, S., Herberz, M., Hahnel, U. J., & Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(1).