The Politics of Policymaking: Issues in Comparative Politics (PUAF U6100). Fall 2018, 2019. [Syllabus]

Policymaking — the process by which political actors make decisions on a range of policy issues — is strongly influenced by context. The political environment in which policymakers interact plays a central role in shaping agendas, strategies, and policy choices. To be successful, policy professionals must be able to navigate a complicated set of political institutions that can constrain the menu of policy options, engage with multiple actors and stakeholders, and become familiar with dynamically changing technological and media environments. This course will give students important background on the way in which political contexts shape policymaking around the world.

Throughout the semester, we will discuss how issues such as state institutions, corruption, misinformation, and political violence influence policymaking around the globe. While the focus of the course is on the developing world, some of the topics we will cover – such as immigration, identity politics, and terrorism – have a growing impact in advanced democracies as well. By the end of the course, students should have an appreciation for the diversity of issues that influence policymaking in a range of contexts, and a better understanding of various pressing global policy issues.

The theoretical concepts and analytical tools covered in this course will draw heavily on quantitative social science research. There are several reasons for that. First, the policy issues discussed in class have inspired excellent academic research that has produced important findings for us to discuss. Second, becoming familiar with quantitative analysis will add an important skill to students’ toolkit. Finally, and relatedly, this will allow us to discuss exciting developments in the frontiers in data science and public policy, and specifically, the way in which ‘big data’ is likely to shape policymaking in a range of policy areas in the future.

In addition to the material covered in the lectures, students will also attend a weekly recitation section. Recitation sections will help students develop the skills necessary for policy analysis, and in particular, policy memo writing.

Data Science and Public Policy (INAF U6506). Spring 2019. [Syllabus]

In our digital age, data are everywhere. According to recent estimates, over 90% of current global data were generated just in the last few years. With internet usage reaching almost half of the world’s population, this trend is likely to increase. The vast amount of information generated by humans, machines, and even nature is becoming increasingly relevant in various policy areas. Social media data are now commonly used to understand – and influence – a broad range of political phenomena; machine learning algorithms increasingly influence decision-making; and high-frequency data allow observing dynamic social and political processes that were harder to detect in the past.

As a result, there is a growing need for policy professionals to understand data science methods, and for data scientists to become familiar with important policy issues. Even though combining policy expertise with data science skills has the potential to produce powerful positive societal outcomes, there are currently few opportunities for policy and data science students to work together.

This course will bridge the gap between data science and public policy in several exciting ways. By drawing on a diverse student body – consisting of students from SIPA and the Data Science Institute – we will combine domain-level policy expertise with quantitative analytical skills as we work on cutting-edge policy problems with large amounts of data.

Throughout the semester, students will have the opportunity to analyze real-world datasets on a broad range of policy topics, including, for example, data on Russian trolls disseminating misinformation on social media, data on Islamic State recruitment propaganda on the Internet, and granular information on natural disasters that can facilitate preparedness for future hazards. In addition, students will work in interdisciplinary policy – data science teams on semester-long projects that develop solutions to policy problems drawing on big data sources. By the end of the course, students will gain hands-on experience working with various types of data in an interdisciplinary environment – a setting that is becoming more and more common in the policy world these days.