Multilateral Moneyball

Contact Info

Erik Voeten

Peter F. Krogh Professor of Geopolitics and Justice in World Affairs

Office: ICC 702

E-mail: ev42@georgetown.edu

Office hours: please schedule online

Course Description and Learning Objectives

The main goal of the Krogh Seminar is for each student to undertake independent research alone or in a small group. This semester the research projects are related to the U.S. State Department’s Multilateral Moneyball project, which is devoted to developing ways in which data-driven analytics can improve the art of multilateral diplomacy. We will visit the State department in January to hear what they are interested in. We will return by the end of the semester to present our findings.

During the first weeks of the semester we will become familiar with tools and analytic approaches. We will especially focus on the analysis of votes and coalition formation in multilateral organizations as well as text analysis. The second half will be devoted to working on individual and/or small group projects. There will be two TAs that will assist the students with technical tasks.

We end the semester with a dinner with a guest who has considerable experience working in one (or more) multilateral institutions. Guests in the past few years were Ivo Daalder (current US representative at NATO), Kemal Dervis (former head of UNDP and finance minister in Turkey), and diplomat extraordinaire Thomas Pickering.

Course Requirements and Grading

  1. Class participation (35%). This is a seminar, not a lecture course. Your contribution is essential. You are expected to come to class prepared. Your participation grade is based on:
  • Active and good participation Active means that you should talk. Good means that I don’t determine your grade based on the number of words spoken but on your substantive contributions to the class discussion. I especially value questions and comments that are informed by the readings. Asking questions is a valid and appreciated means of contributing to class discussions. I expect students to come to class prepared. I also evaluate how each student participates in group work.

  • Peer review on the research of other students. There will be several opportunities for peer review where students can learn from each other. Most importantly these are peer review of research proposals and the presentations. I expect everyone to provide constructive feedback.

  • Short assignments. The early part of the semester has a few short assignments that will be graded for completion. I also expect you to complete the DataCamp courses that I assign.

  1. A Final Project (65%). Everyone will work on a final project related to the Moneyball project. This can be done individually or in small groups. The projects will start with a short research proposal and peer review evaluation. We will devote several class sections to discussing progress. There will be an in-class presentation and a presentation at the State Department. Output for the final project will vary. At a minimum this includes:
  • Two presentations (longer version for class, shorter for State);

  • A short paper describing theory, methods, and the main insights. The length of the paper depends on whether you also produce other output and the size of your group.

Optional elements are:

  • An interactive Shiny app;

  • One or more blog posts about your research that can be featured on Georgetown’s global governance lab site and potentially the State Department’s internal blog;

  • A Github repository with your code and data;

  • An RMarkdown document that details your research and makes it replicable for others;

I do not expect all of these things and we will discuss what makes the most sense for you.

Outline and Class Readings

This is an unusual course and there will be some flexibility in organization. I may move things around as needed and will adjust readings. The web-site is the source for up-to-date information. You will see that there are no assigned books and few assigned readings. We will complete various on-line modules through DataCamp. A lot of this class will be learning-by-doing.

The main software we will use this semester is R. R is open source software and has become the standard for data science and statistics in the social sciences. R is incredibly powerful. It allows you to do sophisticated statistical analyses, create beautiful graphs, make them interactive, and even create on-line apps without really having to learn a lot of coding. You will take on-line DataCamp classes to help you get started. We will also do in-class assignments to work wit State Department data. Two TAs will help you with R.

The first step is to install R and RStudio. RStudio is a really powerful editor from which you can run R. You can do much more with it. In fact, I created this web-site with Rmarkdown in RStudio. This webinar is a really useful way to get started with RStudio. Their web-site has more.

January 10: Introduction to Multilateral Moneyball

In the first class I will offer an introduction of both the substance of the class as well as some of the technical issues.

We will also discuss some basics on United Nations voting.

Voeten, Erik, Data and Analyses of Voting in the UN General Assembly

January 15: MLK Day

January 17: Visit to the State Department

January 22 and January 24: Online progress

  • We will post an on-line lecture Michael Johnson (State Department) and I will give in The Netherlands on the 22nd about the Multilateral Moneyball project

In addition, I expect you to make progress on learning to work with R through the DataCamp courses I am distributing

January 29: What determines UN votes?

Bailey, Michael and Strezhnev, Anton and Voeten, Erik, Estimating Dynamic State Preferences from United Nations Voting Data No need to read technical parts.

Mattes, Michaela, Brett Ashley Leeds, and Royce Carroll. “Leadership turnover and foreign policy change: Societal interests, domestic institutions, and voting in the United Nations.” International Studies Quarterly 59.2 (2015): 280-290.

January 31: Explaining a vote

Finish datacamp courses

In class: practice with merging data and explaining a vote on the Russian invasion of the Ukraine

February 5: Vote buying

Application to Jerusalem vote in UNGA

Browse the following articles (i.e. get a sense of the main argument):

Dreher, Axel, Peter Nunnenkamp, and Rainer Thiele. “Does US aid buy UN general assembly votes? A disaggregated analysis.” Public Choice 136.1-2 (2008): 139-164.

Kuziemko, Ilyana, and Eric Werker. “How much is a seat on the Security Council worth? Foreign aid and bribery at the United Nations.” Journal of Political Economy 114.5 (2006): 905-930.

Dreher, Axel, Jan-Egbert Sturm, and James Raymond Vreeland. “Global horse trading: IMF loans for votes in the United Nations Security Council.” European Economic Review 53.7 (2009): 742-757.

February 7: China and other rising powers

Flores-Macías, Gustavo A., and Sarah E. Kreps. “The foreign policy consequences of trade: China’s commercial relations with Africa and Latin America, 1992-2006.” The Journal of Politics 75.02 (2013): 357-371.

February 12: Basics of text analysis: Working with UN Speeches

February 14: Extracting Preferences from UN Speeches

Becker, Jordan and Edmund J. Malesky. “Atlanticism and Transatlantic Burden Sharing: The Relationship between Strategic Culture and Disaggregated Defense Investment.” International Studies Quarterly. 61 (1): 163-180

February 19: President’s Day

February 21: Analyzing Twitter Data and Sentiment Analysis

Datacamp course TBA

David Robinson Analyzing Trump’s tweets. Here is the RMarkdown document for the analysis.

February 26: Plagiarism in multilateralism

Allee, Todd, and Andrew Lugg. “Who wrote the rules for the Trans-Pacific Partnership?.” Research & Politics 3.3 (2016): 2053168016658919. (focus on this one as we will discuss the example in class)

Daku, Mark and Krzysztof J. Pelc. 2016. “Who Holds Influence Over WTO Jurisprudence?” Working paper. Browse

February 28: Topic models: Classifying Texts

Lucas, Christopher, et al. Computer assisted text analysis for comparative politics. Political Analysis Winter 2015.

March 5 and March 7: Spring Break

March 12: Peer review in class discussion of two-page research proposals

March 14: Interactive visualizations with Shiny Apps

Take the following on-line tutorials: Intro to Shiny

Here are an app we developed to analyze UN votes, display UN votes on a map and one to display ideal points

Here is a variant using Plotly: Simple and Advanced.

Remainder of Semester: Work on Projects (class=LabTime)

Useful Resources

Help with R

We will not read books for this course. There are some substantive articles and you will of course read once you get into your specific topic. In the early part of the course we will spend a lot of time learning how to work with R. R is open source software and there is an incredible user community and a lot of on-line help. There are also two TAs to help you. Here are some good sources.

Data

I will make a folder available on Box that has a lot of the data we need. This includes:

  • The history of UN votes and documentation

  • Ideal point estimates

  • Full text of UN resolutions

  • Full text of UN speeches

  • Data on sponsorship of UN resolutions

Examples of Text Analysis of International Affairs

One of the better established uses of text analysis is to estimate ideological positions of parties based on party manifestoes using the wordscores packages. Check out Ken Benoit’s website for papers and examples.

The NYU Social Media Lab has a lot of examples of applied use of social media analysis in politics

Gary King, Jennifer Pan, and Margaret E. Roberts. 2016. “How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument”. American Political Science Review

Arthur Spirling Leakonomics: analyzing WikiLeaks cables and one more

Lynch, Marc, Deen Freelon, and Sean Aday. “Syria in the Arab Spring: The integration of Syria’s conflict with the Arab uprisings, 2011-2013.” Research & Politics 1.3 (2014): 2053168014549091.

Tumasjan, Andranik, et al. “Predicting elections with twitter: What 140 characters reveal about political sentiment.” ICWSM 10.1 (2010): 178-185.

Richard Nielsen “Can Ideas be ‘Killed?’ Evidence from Counterterror Targeting of Jihadist Ideologues” . Version: 10 August, 2016.

The GDELT project A massive geocoded database of news stories or “events”

Here is the nice intro to R course I mentioned that uses my UN voting data as a case study.

2018-01-29