Outline for today’s class How to do research – Sully Schuster Negotiation practice How to analyze and graph data

Midterm evaluation of teaching feedback More welcoming response to student answers to questions – my apologies on that. More in-class discussion More videos, images, etc.

Why States Take Positions They Do in Int’l Negotiations Ecological Yes Vulnerability No Low Abatement Pusher Bystander Costs High Intermediate Dragger Sprinz and Vaahtoranta, 1994.

Treaty 1 – overarching thoughts Good strategy: look at analysis of similar treaty in chapters in any of the many books I have put on reserve in the library for the course. Imitate that analysis. Polar bears Fisheries Tropical Timber Fur Seals

Questions in a negotiation Who to involve – which states, which non-states What to discuss and how to discuss it (framing) How ambitious to be Means of implementation Response to compliance and noncompliance Different general strategies of negotiation process

Variation in negotiation practice Agreements reached or not No forest agreement; no nitrogen-fixing agreement Many agreements in other issue areas Marine pollution: 11 treaties, 4 protocols, 74 amendments Whaling – annual amendments since 1946 LRTAP: 8 protocols; Montreal: 17 amendments/adjusts Desertification: no additional agreements Strength of agreements Montreal vs. climate Timing of agreements: why in 19xx vs. earlier/later? Approach to agreements: fisheries v. regional v. global

How do choices about what’s in climate change agreement affect whether there is one? Choice #1: Single or comprehensive - is linkage across issues good or bad? Choice #2: Few actors or universal Choice #3: Fixed targets or renegotiable Choice #4: Binding treaty (hard law) or informal guidelines (soft law) Choice #5: World Environmental Organization

Building a Convincing Causal Argument

Building a Convincing Causal Argument Identify important theoretical question Develop hypotheses and identify the variables Select cases to control variables (and thereby exclude rival hypotheses as explanations) Link data to hypotheses Examine correlations and causal pathways Generalize to other cases

A Convincing Argument Requires Evidence that the ACTUAL value of the Dependent Variable matches the PREDICTED value of the Dependent Variable for your theoretical claim AND FOR your counterfactual claim

Final Paper Start by identifying good hypotheses from Brown Weiss/Jacobson readings and others on reserve Compare regulated behavior to otherwise-similar non-regulated behavior to see if they are different If they do NOT differ, there’s nothing to explain (BUT you may want to explain why you expected them to be different) If they DO differ, try to exclude other explanations, leaving the treaty as only remaining explanation Develop counterfactuals to evaluate whether changes were due to treaty or other factors

Potential Independent variables

Possible Comparison #1: Members/Non-members Members (regulated actors) to non-members (non-regulated actors), Members vs. non-members after treaty Treaty members before/after treaty starts

Possible Comparison #2: Regulated/Non-regulated Activity Members regulated activity to members non-regulated activity Catch of regulated yellowfin tuna vs. non-regulated bluefin tuna Sulfur dioxide pollution vs. carbon monoxide pollution

Possible Comparison #3: Regulated/Non-regulated Location Members in regulated location to members in non-regulated location Catch of yellowfin tuna in regulated area (Indian Ocean) vs. non-regulated area (Western Pacific) Pollution of regulated river vs. pollution of non-regulated river

Pollution Example

Pollution Example Treaty In Force

Pollution Example Treaty In Force COUNTERFACTUAL 1: Predicted Emissions if Treaty Wasn’t Signed (using Members)

Pollution Example Treaty In Force COUNTERFACTUAL 1: Predicted Emissions if Treaty Wasn’t Signed (using Members)

Pollution Example Treaty In Force COUNTERFACTUAL 2: Predicted Emissions if Treaty Wasn’t Signed (using Non-Members)

Problems of graphing data and how to resolve them

Data Analysis Strategies, aka: “How to Drink from a Firehose” Determine ideal DV before looking at data Draw expected graph by hand: years, lines included, success example Identify best indicators you have available Compare regulated/non-regulated behavior Actors: Member/non-member Regulated countries vs. non-regulated countries Activity: Regulated/non-regulated activity E.g., regulated chemical/species vs. unregulated chemical/species Location: Regulated/non-regulated geographic area E.g., catch in North Pacific vs. South Pacific

Simplify your data!! DON’T USE ALL THE DATA YOU HAVE! Focus on most important indicators Focus on 15 years before / 15 years after Focus on countries most responsible Create columns for regulated/non-regulated Compare average of country groups No more than 6 lines on one graph Think about normalizing data across countries: Index // percent change // per GDP // per person Graph alternative explanations, e.g., price

Graphing without thinking: don’t know what you’re seeing Goal: See if treaties change country behavior over time Draw what you want by hand, then graph Country-groups, X-axis, Y-axis, expected lines Practice makes perfect – do it by hand again!

Graphing everything creates spaghetti Large countries swamp small Too much to understand Graph all; then delete one-by-one to learn about your data: major polluters, missing data, etc. Create groups of different type states Members vs. Non-members Developed vs. Developing members Green vs. Brown members Other categories theory suggests Make sure data isn’t messing you up

Graphing the raw data doesn’t make countries similar enough Solution 1: Indexing Concept: view each country’s behavior as % of its behavior in year treaty entered into force (EIF year) Doing it: copy of rectangle, but with formula that divides each country’s data in each year by that country’s data in the EIF year Solution 2: Normalizing Concept: view each country’s behavior after adjusting for population, GDP, or other variables Doing it: copy of rectangle, but with formula that divides each country’s DV by IV in each year