The June 2017 issue of the Economic Journal has a paper entitled “Assignment procedure biases in randomized policy experiments” (ungated version). The abstract summarizes the claim of the paper:
“We analyse theoretically encouragement and resentful demoralisation in RCTs and show that these might be rooted in the same behavioural trait –people’s propensity to act reciprocally. When people are motivated by reciprocity, the choice of assignment procedure influences the RCTs’ findings. We show that even credible and explicit randomisation procedures do not guarantee an unbiased prediction of the impact of policy interventions; however, they minimise any bias relative to other less transparent assignment procedures.”
Of particular interest to our readers might be the conclusion “Finally, we have shown that the assignment procedure bias is minimised by public randomisation. If possible, public lotteries should be used to allocated subjects into the two groups”
Given this recommendation, I thought it worth discussing how they get to this conclusion, and whether I agree that public randomization will minimize such bias.
- In this week’s Science, Rema Hanna, Gabriel Kreindler, and Ben Olken look what happened when Jakarta abruptly ended HOV rules – showing how traffic got worse for everyone. Nice example of using Google traffic data – MIT news has a summary and discussion of how the research took place : “The key thing we did is to start collecting traffic data immediately,” Hanna explains. “Within 48 hours of the policy announcement, we were regularly having our computers check Google Maps every 10 minutes to check current traffic speeds on several roads in Jakarta. ... By starting so quickly we were able to capture real-time traffic conditions while the HOV policy was still in effect. We then compared the changes in traffic before and after the policy change.”All told, the impact of changing the HOV policy was highly significant. After the HOV policy was abandoned, the average speed of Jakarta’s rush hour traffic declined from about 17 to 12 miles per hour in the mornings, and from about 13 to 7 miles per hour in the evenings”
- From NPR’s Goats and Soda: 4-year kids of Cameroonian subsistence farmers take the marshmallow test, as do German kids – who do you think did best?
- How to teach development economics in 20 minutes to 7th graders – Dave Evans explains his method.
- The “beginning at the end” approach to experimentation – written from the point of view of business start-ups, but could easily apply to policy experiment work too “The typical approach to research is to start with a problem. In business, this often leads to identifying a lot of vague unknowns—a “broad area of ignorance” as Andreasen calls it—and leaves a loosely defined goal of simply reducing ignorance…“Beginning at the end” means that you determine what decision you’ll make when you know the results of your research, first, and let that dictate what data you need to collect and what your results need to look like in order to make that decision.”
I received an email recently from a major funder of impact evaluations who wanted my advice on the following question regarding testing baseline balance in randomized experiments:
Should we continue to ask our grantees to do t-tests and f-tests to assess the differences in the variables in the balance tables during the baseline?
An exciting new place for lots of discussion on policy issues in development economics – this week VoxDev launched. Some of the opening posts include:
- Banerjee and Kala on the effects of India’s demonetarization
- Glaeser on housing bubbles in China
- Goldberg and Pavcnik on globalization in developing countries
- Dupas on delivering health subsidies in developing countries
- Acemoglu and Restrespo on the race between humans and machines for jobs
- If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?
- How many of these children would you like to be boys, how many would you like to be girls, and for how many would it not matter if it’s a boy or a girl?