Running Economics Experiments
Economic experiments in the lab and in the field allow for transparent and compelling causal estimation. MIT's Jameel Poverty Action Lab (JPAL) offers a five-day seminar that teaches the methods of randomization including choosing an appropriate sample size and how to address common threats to the experiment,
MIT Open Courseware provides these trainings in video and readings here and are available below.
It's unclear what becomes of experimental economics. Any monkey can run an experiment and the job market seems to punish students who invest heavily in experimental papers. And the method trades clear identification for clear external validity. Angus Deaton at Princeton is not a fan of economic experiments and explains his reasons here. Guido Imbens at Harvard disagrees and responds here.
Basic in running experiments is the difficulty of choosing an appropriate sample size. I have a useful tool here for doing so for almost every design. Don't mess to much with figuring everything out because it can take a lot of time. The tool here allows you to just input the relevant information about your data and experiment without getting waist-deep in power calculation formulas.
Topics covered: What is economic evaluation?
Needs assessment
Process evaluation
Impact evaluation
Economic cost-benefit analysis
Instructor: Rachel Glennerster
Topics covered: Why randomize in economics?
Instructor: Dan Levy
Topics covered: How to Randomize I
Methods of randomization: Lottery, Phase in, Rotation, encouragement
Multiple treatments
Gathering support
Instructor: Dean Karlan, economist at Yale
Topics covered: How to Randomize II
Unit of randomization
Cross-cutting treatments
Stratification
Mechanics
Instructor: Rachel Glennerster
Topics covered: Measurement and Outcomes
Key hypotheses
Primary and intermediate outcomes
Interpreting multiple outcomes
Theory of change Model
Questionnaire design
Data collection/entry
Instructor: Esther Duflo, economist at MIT
Topics covered: Sample Size and Power Calculations
Econometric estimation
Hypothesis testing
Power: significance level, variance of outcome, effect size
Clustered design
Instructor: Ben Olken, economist at MIT
Topics covered: Managing threats to evaluation and data analysis
Attrition
Externalities (spillovers)
Partial compliance and selection bias
Instructor: Michael Kremer, economist at Harvard
Topics covered: Analyzing Data
Intention to treat (ITT) and Treatment on treated (ToT)
Choice of outcomes and covariates
External validity
Cost-effectiveness
Instructor: Shawn Cole
Key Words: Economics, experiments, randomized-control trials, RCT, Andrew, Johnston, Andrew Johnston, economist, empirical, microeconomics, field research, lab research, experimental economics