International Relations

Taught: Winter 2023


December 20, 2022

Course information

Course description

This course is a graduate-level introduction to the field of international relations (IR), focusing on core theoretical and empirical debates in the literature. Readings are a mix of old and new, leaning towards newer work.

The primary goal of this course is to for students to develop a sense for where the literature has been and where it is going. As students move forward with their studies, this course will help students develop a fuller sense of the context in which other work they read is situated.

Course policies

Late proposals, papers, etc. will be penalized by a letter grade per day, up to two days, then it’s a zero.


Participation (5% of grade)

To receive full credit you must:

  1. come to class having read

  2. be engaged in discussion

Discussion questions (DQs; 10% of grade)

Eeach week you will post one bullet point discussion question/comment per reading to Canvas. The questions/comments need to be good. A good discussion question offers a specific critique, is thought-provoking, generates discussion among the group, etc. A not-good discussion question is above all generic – it is not specific to the reading and leaves the group without guidance on where to take the discussion.

⭐ Due dates: Sunday by midnight before each class

Peer-review (30% of grade)

You will write two mock “peer-reviews” in the spirit of working as a reviewer for a journal. Your review should eschew summary in favor of critically evaluating the paper (a great guide here). The goal is to make a recommendation to an editor (me) as to whether or not the paper should be published. If your recommendation is to “revise and resubmit” assume that implies a high probability of publication. The review should be between 500 and 800 words in length.

Due dates:

  • Review 1 due by February 3rd, 2023 ⭐
  • Review 2 due by March 3rd, 2023 ⭐

Choose two from the following:

Dataset presentation (25% of grade)


The goal of this assignment is to become acquainted with an important data collection effort in our field. You will download the data, clean it as needed, learn its ins and outs, and produce some compelling visuals. You will then present on the dataset to the class for ~ 10 minutes. Points to hit in the presentation:

  • What does an observation look like in this data? What is being measured? How do the authors get from raw text to the final product? (~ 1 slide)
  • You will create and discuss two interesting visualizations from this dataset; could be time series, bivariate relationship, conditional means, a table, etc. (2 slides)
  • Strengths and weaknesses – what is good and exciting about this project? What kinds of questions could we answer with this data? What are the limitations (temporal, spatial, conceptual)? (~ 1-2 slides)

See Data sets for a list of ideas. Below are also some good templates for making interesting visualizations of datasets:


You will work in pairs and submit jointly (sign up sheet in class). Two outputs: an R script with the code used to make visuals/tables + the presentation. Each pair of students will email me these shortly after their presentation.

Research proposal (30%)

6 pages. See the rubric. You should meet with me to talk through your idea, sooner rather than later.

⭐ Due by March 21, 2023.


Note: the schedule is subject to change.

Week 1 – Jan 10 : What does good research in IR look like? Theory, causality, prediction


Week 2 – Jan 17 : What are the fundamental characteristics of the international system? Cooperation and conflict under anarchy


Week 3 – Jan 24 : Why war? Bargaining and information


Week 4 – Jan 31 : How and when do states intervene in each other’s affairs? Third-party interventions


Week 5 – Feb 7 : How does national politics shape foreign policy? Domestic audiences and incentives for war


Week 7 – Feb 21 : How much do leaders matter? Leaders, autocrats, regimes


Week 8 – Feb 28 : Do interstate ties constrain state behavior? Networks


Week 9 – Mar 7 : When do states cooperate? Agreements and IOs


Week 10 – Mar 14 : Borders, territory, migration


Extra stuff, no room


state capacity



Power, deterrence, reputation

Repression and protest



Data ideas

Here’s a (non-exhaustive) list of datasets you might consider:

  • The COW datasets
  • LEAD (leader characteristics dataset)
  • Minorities at Risk project
  • Social Conflict Analysis database
  • Global Terrorism Database
  • Political Instability Task Force
  • Quality of Governance data
  • Varieties of Democracy (VDEM)
  • Transparency International
  • AID Data (William & Mary)
  • Nonviolent and Violent Campaigns
  • Change in Source of Leader Support (CHISOLS) Data
  • The Varieties of Coups D’état: Introducing the Colpus Dataset
  • Electoral intervention dataset 1946–2000
  • Bread before guns or butter: Introducing Surplus Domestic Product (SDP)
  • Yes, Human Rights Practices Are Improving Over Time
  • Introducing the Military Intervention Project: A New Dataset on US Military Interventions, 1776–2019
  • DCAD dataset
  • Conflict Events Worldwide Since 1468BC: Introducing the Historical Conflict Event Dataset
  • frozen conflicts in world conflict dataset
  • UN Fatalities dataset
  • Partisan electoral interventions by the great powers: Introducing the PEIG Dataset
  • near crises in world politics
  • Post-Cold War sanctioning by the EU, the UN, and the US: Introducing the EUSANCT Dataset
  • Threat and imposition of economic sanctions 1945–2005: Updating the TIES dataset
  • Financial contributions to United Nations peacekeeping, 1990–2010: A new dataset
  • The Autocratic Ruling Parties Dataset: Origins, Durability, and Death

Final research design rubric

6 pages MAX, double-spaced.

  1. Introduction (1 pages)
    1. Motivate why we should care about the question you want to answer (e.g., because of its real-world impact, as a gap in the literature)
    2. BRIEFLY preview what the project will do (I will argue that X) and how it will do it (I will collect XYZ data)
  2. Literature review (2 pages)
    1. Briefly describe what we already know about your topic
    2. Highlight what is unknown or what gap your project will fill
  3. Theory + hypotheses (3 pages)
    1. Big note: theory != literature review
    2. Need an argument about a causal process
    3. Think about who the actors are in your story, what they want, and how their interactions produce different outcomes

Reviewing a paper

(Copied this from Macartan Humphreys’ website which seems to be down)

For a formal review or referee report you have space to go into much more depth. A standard approach is to divide these reviews into three parts.

The first part can be a single paragraph — it summarizes the key contribution of the paper as you see it, gives an overall assessment, and points to the key issues, concerns, or strengths. Don’t forget the strengths. Try to articulate succinctly what you know now that you didn’t know before you read the piece. Often a quick summary can draw attention to strong features you were not conscious of, or makes you realize that what you were impressed by is not so impressive after all.

The second part discusses 3 – 6 major features of the paper; the checklist below lists features that could be useful to think through when selecting themes. Try to organize by theme (measurement, explanation etc.). The third part is for “smaller issues” where you can bullet point things from ambiguities, to estimation issues, to pointers to other work.

Other things:

  • It’s useful to authors when you can point to literature they have not read, if relevant.
  • It’s useful to authors to know what to cut: reviews tend to worry about length but still ask for more.
  • Your tone should be such that you would not feel embarrassed if someday your review gets into the public domain by mistake.
  • You should feel free to ask for extra material such as replication data or analysis plans. Sometimes reviewing can go quicker if you can access data.
  • Don’t ask the authors to ask and answer a different question; respond to the paper you have been sent.
  • Be generous: share references if they are missing but don’t assume that researchers intentionally ignored the work of others (or your work!); raise ethical issues if you see them but don’t assume researchers acted without ethical concern; ask for multiple comparisons corrections but don’t assume deliberately misleading reporting.
  • Pronouns. For anonymous review it’s usually safe to use pronouns “you” or “they” even if single authorship has been indicated.

The Checklist

Here is my list of what to look out for as I read a paper:


  • Is the theory internally consistent?
  • Is it consistent with past literature and findings?
  • Is it novel or surprising?
  • Are elements that are excluded or simplified plausibly unimportant for the outcomes?
  • Is the theory general or specific? Are there more general theories on which this theory - could draw or contribute?

From Theory to Hypotheses

  • Is the theory really needed to generate the hypotheses?
  • Does the theory generate more hypotheses than considered?
  • Are the hypotheses really implied by the theory? Or are there ambiguities arising from say - non-monotonicities or multiple equilibria?
  • Does the theory specify mechanisms?
  • Does the theory suggest heterogeneous effects?


  • Are the hypotheses complex? (eg in fact 2 or 3 hypotheses bundled together)
  • Are the hypotheses falsifiable?

Evidence I: Design

  • External validity: is the population examined representative of the larger population of - interest?
  • External validity: Are the conditions under which they are examined consistent with the - conditions of interest?
  • Measure validity: Do the measures capture the objects specified by the theory?
  • Consistency: Is the empirical model used consistent with the theory?
  • Mechanisms: Are mechanisms tested? How are they identified?
  • Replicability: Has the study been done in a way that it can be replicated?
  • Interpretation: Do the results admit rival interpretations?

Evidence II: Analysis and Testing

  • Identification: are there concerns with reverse causality?
  • Identification: are there concerns of omitted variable bias?
  • Identification: does the model control for pre treatment variables only? Does it control or - does it match?
  • Identification: Are poorly identified claims flagged as such?
  • Robustness: Are results robust to changes in the model, to subsetting the data, to changing - the period of measurement or of analysis, to the addition or exclusion of plausible - controls?
  • Standard errors: does the calculation of test statistics make use of the design? Do standard - errors take account of plausibly clustering structures/differences in levels?
  • Presentation: Are the results presented in an intelligible way? Eg using fitted values or - graphs? How can this be improved?
  • Interpretation: Can no evidence of effect be interpreted as evidence of only weak effects?

Evidence III: Other sources of bias

  • Fishing: were hypotheses generated prior to testing? Was any training data separated from test data?
  • Measurement error: is error from sampling, case selection, or missing data plausibly - correlated with outcomes?
  • Spillovers / Contamination: Is it plausible that outcomes in control units were altered - because of the treatment received by the treated?
  • Compliance: Did the treated really get treatment? Did the controls really not?
  • Hawthorne effects: Are subjects modifying behavior simply because they know they are under study?
  • Measurement: Is treatment the only systematic difference between treatment and control or are there differences in how items were measured?
  • Implications of Bias: Are any sources of bias likely to work for or against the hypothesis tested?


  • Does the evidence support the particular causal account given?
  • Are mechanisms examined? Can they be?
  • Are there observable implications we might expect to see associated with different possible mechanisms?

Policy Implications

  • Do the policy implications really follow from the results?
  • If implemented would the policy changes have effects other thank those specified by the research?
  • Have the policy claims been tested directly?
  • Is the author overselling or underselling the findings?