Experiments in Democracy, November 14th.docx

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16 Apr 2012
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Experiments in Democracy
Milk or tea first?
Robert fisher did an experiment where 4 cups had the milk in first and 4 with the tea in first
Dr.Bristol identified all 8 cups correctly
Was she lucky?
How could we know?
Fisher developed the exact test
The probability that one can get all 8 cups correct is 1 in 70
SO what’s important?
- We can learn from experiments
- We can test people’s claims
- Treatment and control
- Randomization
- Measurement
Game Plan
- Some important questions
- What do we mean by experiment? And why do we do them?
- Types of experiments
- Federalism and representation
- Clientelism and voting
- Facial competence and voting
- A chance to complete experiments for extra marks
Some important questions
- Does federalism lead to better political representation
- Are voters superficial
- How do we stop patronage and instead get public goods
- Does contact from political campaigns matter in an election
Defining Experiments
- Treatment/Conditions
- Assignment to treatment or control/comparison groups is random and the process of
randomization is known
- Ex-post measurement of results
- Example: does prof. kopstein teach better when he wears a tie?
- Treatments: A treatment is a stimulus
- Anything that is thought to havr an effect on some outcome
- It is applied to some people and not to others
- Or, it is applied at some times and not others
- Example: whether prof.kopstein wears a tie to class to teach
- Random assignment:
- We put some subjects in treatment and some in control
- We do it randomly
- What does it mean that this is done randomly
- Example: flip a coin before every class to see whether prof.kopstein would wear a tie
- Ex-post measurement of results:
- -For an experiment to be complete, we need to measure the results
- What we want to measure are causal effects
- Example: we could measure exam performance at the end of the year
Example:
- Question: does wearing a tie make prof.kopstein teach better
- Treatment: wearing a tie or not
- Random assignment: flip a coin before class
- Measurement: exam results
- To make a causal inference, we must have all three parts
Measuring effects
- What is a causal effect?
- The difference in outcomes between two states of the world, treated versus untreated
- - Ti-yi(reated)-Yi(untreated
- Ti=yi(class with a tie)-yi(class with no tie)
- Why is this problematic
- -we cannot observe any given subject in both its treated and untreated state.
- Fundamental problem of causal inference
- Random assignment enables us to create two groups whose treated and untreated states are
the same in expectation
In the example, causel effect: exam results (tie)- exam results (without a tie)
Why do an experiment?
- Because we want to make causal inferences
- But we want to make them about important things.
1. Does federalism increase representation
2. Can politicians win by providing patronage or public goods?
Field experiment
Examples:
- You could assign some people to receive emails telling them to vote
- You could give some people free tuition and see if it increases or decreases school performance
- More examples to come
Natural experiments
- Sometimes the world provides for experiments
- - natural disasters
- Lotteries
- School vouchers
- If we can measure the effects of these, we can conduct natural experiements
Survey/lab experiemtns
- Survey and lab experiments provide treatments in less natural environments
- They could be done over the phone, or online, or in a computer room
- Treatments are often different wordings of a question, or exposure to a picture, or video
content
Federalism and helpfulness
- Federalism generates jurisdictional boundries
- There is federal government and provincial governments
- Some responsibilities are unique, but some are shared
- These are not always clear, especially to voters
- Who do you contact for help?
Federalism experiment
- Subjects: randomly choose 101 federal and 101 provincial politicians
- -treatments: send politicians emails from “citizens” see how the politicians respond
- Employment insurance, finding a family doctor, student loans
- We also randomize “citizen’s” characteristics, including gender, ethnicity and past political
support
Treatment: is the request in an IN, OUT or shared jurisdiction
Randomization: politicians are randomly selected to receive some messages and not others
Measurement: compare how helpful politicians were.