Now that you have your groups, I’d like you to start working on your pre-analysis plan
I’ll give you some time to discuss your ideas and start writing your plan
My plan is that you should slowly think about your final project, one step at a time
What do you think about having two weeks to write the following:
Submit at most 2 paragraphs summarising an experiment that you want to develop in this course. At minimum, your summary should include a research question, why the question is important, and a rough sketch of how you plan to answer the question.
In three weeks:
Write a title and abstract for a paper you imagine writing based on your proposed experiment. Assume that your findings align with your theoretical predictions. Remember to establish why the findings matter for your intended audience.
In four weeks:
Outline your pre-analysis plan. Your outline should include sections on the research question, the experimental design, the data you will collect, and the analysis you will conduct.
What I have in mind…
In five weeks:
Use Quarto and DeclareDesign to write your report and simulate your experiment
In six weeks:
Revised outline, now including a new section titled “Potential Threats.” In this section, diagnose threats and briefly describe potential countermeasures. This new section should discuss false positives, statistical power, demand effects, noncompliance, spillover, and attrition.
In seven weeks:
Revised outline, now included a new section on “Heterogeneous Treatment Effects.” In this section, discuss how you might explore heterogeneity in treatment effects. This section should include a discussion of how you might use covariates to explore heterogeneity.
In eight weeks:
I will give you feedback on your outline and provide you with the simulated data for your experiment.
You will have two weeks to write your final report.
Last week of class:
You will present your findings to the class.
R packages 📦
fabricatr: Simulating data for experiments
fabricatr is a very useful package!
You guys have no idea how much time (and money) it has saved me 😅
It is straightforward to use and has a bunch of functions
library(randomizr)N <-100Z <-complete_ra(N = N, num_arms =2)head(Z)
[1] T2 T1 T1 T1 T1 T2
Levels: T1 T2
# This makes a cluster variable: one unit in cluster "a", two in "b"...clust_var <-rep(letters[1:15], times =1:15)Z <-cluster_ra(clusters = clust_var,m_each =c(4, 4, 7),conditions =c("control", "placebo", "treatment") )table(Z, clust_var)
clust_var
Z a b c d e f g h i j k l m n o
control 1 0 0 4 0 0 7 0 9 0 0 0 0 0 0
placebo 0 0 3 0 0 6 0 8 0 0 0 12 0 0 0
treatment 0 2 0 0 5 0 0 0 0 10 11 0 13 14 15
estimatr: Estimating treatment effects
We have already seen estimatr in action
The package is particularly useful to estimate linear models with robust standard errors
Economists use OLS for everything 😅
But you can also use it to estimate instrumental variables models, difference-in-differences, and more
estimatr integrates well with the tidyverse, so you can use it with dplyr, ggplot2, and other packages
Do campaign donations secure preferential treatment from policy makers?
Challenge: It is hard to isolate the effect of donations on policymakers’ behaviour
First randomised field experiment on campaign contributions and access
Do donations facilitate access to influential policy makers?
The Experiment
A political organisation tried to schedule meetings between congressional offices and their members (who were donors)
The organisation randomly revealed to offices if attendees were donors
Key finding: When informed attendees were donors, policymakers were available 3-4 times more often
Underscores concerns about campaign finance deregulation
Why Should We Care?
Political Inequality: Campaign donations may amplify the voices of the wealthy
Policy Decisions: Understanding who has access informs how policies are made
Democracy: This research is crucial for a more equitable political system
Policy Relevance
Campaign finance reform: Findings can inform debates about the necessity of these reforms
Regulation: This study helps to clarify the relationship between campaign donations and access to power
Supreme Court decisions: Results bear on recent deregulation (at the time)
Key Debates
Key Questions
Are campaign contributions a form of speech, or a form of exchange/contract?
Does the ‘marketplace of ideas’ become skewed when one side is better funded?
Primary Hypotheses
The Core Idea: Revealing that prospective attendees are donors will increase the likelihood of meetings with senior officials.
H1: Senior policy makers will make themselves available more often when they know that prospective attendees are political donors.
H2: That knowing about donations matters more when scheduling meetings with higher level officials.
The Logic
Access is a Resource: Policymakers’ time is a finite and scarce resource
Donors Signal Value: Donations can be perceived as signals of shared interests, expertise, or future support
Potential for Reciprocity: Policymakers may see potential gains in meeting with those who have demonstrated political activity
Conditional effects: The effect may be stronger when meetings are requested with the most senior staff
More senior staff means more important political power
Intervention
Intervention
A grassroots political organisation attempts to schedule meetings with congressional offices
Whether congressional offices are informed that attendees are political donors
Control Condition: Offices are informed the meeting attendees are “local constituents”
Outcome Measurement
Primary Outcome: The seniority/level of the official who agreed to attend the meeting
Rank Order: The access level was ranked from 1 (most desirable: member of Congress) to 6 (least desirable: no meeting)
Data Collection: The level of staff that agreed to the meeting was collected
Meeting: Attendees confirmed that the promised staffer attended the meeting
Random Assignment
Blocking: The researchers blocked congressional offices into triplets based on factors that could be associated with legislative access (prior voting record, cosponsorship of the bill, years of service, ideology, local population)
Randomisation: within the blocks, offices were randomly assigned to the ‘donor revealed’ condition.
191 congressional offices were contacted, 96 in the treatment group and 95 in the control group
Treatment
Follow-up
Results
Results (cont.)
Results (cont.)
Discussion
Implications
The study provides evidence that campaign donations can facilitate access to policymakers
The findings underscore concerns about the influence of money in politics
The results suggest that campaign finance reform may be necessary to ensure a more equitable political system
Limitations
Mechanisms: The exact reasons why legislators reacted to donations needs further study
Causal Chain: The link between access and influence requires further investigation
Generalisability: More studies with different actors, organisations and contexts are required
Bertrand and Mullainathan (2004) 🇺🇸
Bertrand, M., & Mullainathan, S. (2004)
Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination
