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Properly balancing panel data and removing duplicate unique identifiers is a recurring challenge for students in my Research Practicum course. Accordingly, I thought it would be helpful to provide an example for everyone.
Hello, world! I have finally figured out GitHub and how to make a free website with Jekyll. It’s a great day!
Do multilateral foreign aid institutions allocate their resources in ways that are more consistent with their stated mandates or rather with their donors’ strategic interests to trade aid for influence and policy concessions? In this paper, I argue that dynamics between donors (principals) and institutions (agents) render multilateral aid less prone to capture by principals’ strategic interests than most literature suggests. High monitoring costs, agents’ preferences for survival, and principals’ actions to secure agent survival especially since the end of the Cold War underpin my argument. To support my argument about agent autonomy, I leverage new data on how staff at the World Bank, African Development Bank, Asian Development Bank, and Inter-American Development Bank rank the institutional environments of recipient countries. These data provide a crucial test for my argument because these international organizations purportedly use these institutional ratings data to determine the creditworthiness of recipient countries. Using numerous model specifications relating to the number of loans allocated and money received, I find broad support for my argument on agent autonomy. Especially since the end of the Cold War, countries with positions of power in the international system are less able to pick the winners and losers of development through multilateral aid than most literature suggests. The results of this study contribute to emerging literatures showing that ``aid is not oil’’, that multilateral aid is less prone to capture than bilateral aid, and that bureaucratic factors are crucial for understanding international organizations and foreign aid.
For about 25 years, empirical scholarship on corruption has primarily relied on perceptions data, but the drawbacks of these measures are ample and well-known. More recently, analyses centered on Brazil have showcased the utility of randomly assigned audits as a more objective alternative to perception-based measures. However, Brazil is the only country with randomized audits and has many unique institutional features that limit the external validity of the numerous studies using the Brazil data. In this paper, I provide a new framework to assess the quality of audit data even when they are not randomly assigned. Specifically, I show that it is acceptable to use experimental or observational audit data to measure corruption when: 1) the auditing institution is legally independent from the executive branch; 2) the distribution of audits is not biased against opposition party politicians, especially following close elections; and 3) the intensity/dosage is consistent across similar types of audits. I demonstrate the utility of the framework by analyzing a massive new dataset of subnational audits from India, Mexico, Honduras, and Guatemala. The new data and framework proposed in this paper will help researchers undertake more objective analyses of governmental corruption around the world. [Presentation]
Invited to submit at Annual Review of Political Science
(with Mike Findley and Kyosuke Kikuta)
Monitoring Corruption and Overcoming the Collective Action Problem: Experimental Evidence from Pakistan
(with Torben Behmer, Mobin Piracha, and Adi Tantravahi)
Revise and Resubmit at Journal Conflict Resolution
(with Mike Findley, Joelean Hall, Andy Stravers, and Jim Walsh)
(with Akshat Gautam)
Have you ever wondered how to make a map in ArcGIS? In this tutorial, we cover all of the basics: coordinate systems, how to work with shapefiles, spatial joins, and much more. By the end of the workshop, you will be able to plot point data and color in your map based on spatial polygons values using ArcGIS’ symbology function. [Slides Here]
Instructor, University of Texas at Austin, Department of Government (GOV 355D), 2019
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 2020 Evaluation] [Spring 2019 Evaluation]
Instructor, University of Texas at Austin, Department of Government (355C), 2019
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] [Latest Evaluation]
Instructor (with Mike Findley), University of Texas at Austin, Department of Government (GOV 355M), 2020
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]