Teaching

Quantitative Methods for Public Management 2

Instructor, Texas A&M University, Bush School of Government and Public Service (Bush 635), 2025

This course provides students with the knowledge that they will need to attain an entry-level research analyst position in the international development space. Most quantitative methods courses that are the second in a sequence for Master’s degrees in international affairs and public policy focus mostly on causality/internal validity. By contrast, in this course we complement a focus on causality with an all equal focus on external validity. That secondary focus on external validity is crucial: students will need to be able to use the tools that they learn in this course to answer policy-related questions in their future jobs. The course begins with an introduction to directed acyclic graphs (DAGs), because we need to learn to draw our assumptions before we draw our conclusions. Then, the course examines selection bias and how to correct for it in experiments. Next, we learn about instrumental variable models and standard natural experiments. In the second part of the course, we examine panel data regression, multilevel models, difference-in-differences, and matching. In the final part of the course, students learn about synthetic control and counterfactual estimators, regression discontinuity designs, and geomapping and spatial regression. The final class also covers ordered outcomes and count models. [Syllabus]

Quantitative Methods for Public Management I

Instructor, Texas A&M University, Bush School of Government and Public Service (Bush 631), 2024

We live in an era of data-driven decision-making, and quantitative evidence is fundamental to inform sound governmental policies on both domestic and international issues. This course provides an introduction to quantitative methods for public policy, equipping students with fundamental skills to critically consume and analyze quantitative evidence in international development and security. Upon successful completion of the course, students will be able to: (1) conduct basic descriptive inference, statistical inference, linear regression, and prediction, using the statistical software program R and, to some extent, MS Excel; and (2) explain the basics of causal inference, using causal diagrams, randomized experiments, and other quasi-experimental methods. [Syllabus]

Foreign Aid

Instructor, Texas A&M University, Bush School of Government and Public Service (Bush 689), 2024

This Master’s-level course provides an overview of the role of foreign aid in international development. The course will begin by surveying the major development paradigms/frameworks and their pitfalls: modernization, dependency theory, and principal-agent theory. Then, we will examine the major ideas, including the Washington Consensus, the importance of institutions, searchers vs. planners, community-driven development, social protection, and the rise of randomized control trials. In the process, the course will explore the role of the various actors (e.g., World Bank, IMF, regional development banks, traditional bilateral donors, China), relevant political economy considerations, and the long-run efficacy of foreign aid. To tackle the latter, the course will notably explore the complicated relationship between foreign aid and corruption—not just at the theoretical level, but also through exploration of auditing, procurement, and social accountability controls. When possible, I will leverage my contacts in academia and aid agencies to provide students with opportunities to engage with authors of the works that we will read and/or provide a first-hand policy perspective. [Syllabus]

Applied Research: Political Science (Research Practicum, Semester 2)

Instructor, University of Texas at Austin, Department of Government (GOV 355D), 2022

This course is the second semester of a Research Practicum program that attempts to provide undergraduates with a fairly comprehensive introduction to the research process in the social sciences. Classroom instruction covers experiments, data structures, data cleaning, hypothesis testing, measurement challenges, linear regression, as well as the basics of panel data, regression discontinuity designs, difference-in-differences, synthetic controls, logistic regression, and network analysis. Training in Stata, R, LaTeX, Mendeley, and ArcGIS continues during the second semester of the course as well. At the end of the second semester, students complete their own research projects, write-up their results in a formal paper, and present their findings to the class. [Syllabus] [Spring 2022 Evaluation] [Spring 2021 Evaluation] [Spring 2020 Evaluation] [Spring 2019 Evaluation]

Applied Research Methods 1 (Research Practicum, Semester 1)

Instructor, University of Texas at Austin, Department of Government (GOV 355C), 2021

This course is the first semester of a Research Practicum program that attempts to provide undergraduates with a fairly comprehensive introduction to the research process in the social sciences. Classroom instruction covers arguments, concepts, measures, causality, and basic statistics. Given that knowledge of statistical software, text editors, reference management software, and mapping software is increasingly helpful for success in the social sciences, the course will also provide training in Stata, R, LaTeX, Mendeley, Excel, and ArcGIS. At the end of the first semester, students will hand-in their own well-developed Research Proposals in lieu of a final exam. [Syllabus] [Fall 2021 Evaluation] [Fall 2019 Evaluation]

Data Science for the Social World

Instructor (with Mike Findley), University of Texas at Austin, Department of Government (GOV 355M), 2021

This course provides students with a comprehensive introduction to data science for the political and economic world. By focusing on practical data skills coupled with strong social science reasoning, the course will enable students to acquire skills that will help them prepare for jobs in data science, industry, and academia. Organized around a set of substantive themes and practical tasks, each class topic is motivated by real-world problems and then backed with data science skills to solve those problems. Emphasis is placed on developing proficiency in cleaning, manipulating, wrangling, scraping, visualizing, and mapping data. Most work is conducted in the software programs R and Excel, and to a lesser extent through introductory exercises in other programs. In the process, students learn about good principles of working with data, including through version control with Github. The class takes place through asynchronous instruction, online coding practice problems, exams, and online instructor consultations. [Syllabus] [Summer 2021 Evaluation]

Mike Denly