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Field
Last Published
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Before
August 31, 2018 01:11 AM
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After
August 31, 2018 06:57 PM
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Field
Randomization Method
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Before
I do a simple randomization at the individual shop level. For the first round of emails, I randomly assigned one of the eight treatment groups (based on the combinations of perceived gender and customer type) to each shop. I determined the distribution of customer types using the optimal shares obtained in the power calculations. I assigned 18.3% standard scripts, 15.7% low-search cost scripts, 8% high-education and 8% uninformed scripts to each gender.4 I also randomized which other characteristics are used to contact each shop. These characteristics are the script within each customer type, the email subject and the car year. The scripts and email subjects provide the same information but are reorganized and slightly differently worded, and the car years are 2009 and 2010, which belong to the same car generation.
The purpose of these additional variations is to avoid sending a second email that is identical in all but customer’s name and type.
For the second round of emails, I will keep all valid auto repair shop emails. I will change the gender and customer type with respect to that used for the first email, restricting the within shop comparison to always include a standard type-script.
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After
Randomization done by computer
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Field
Sample size (or number of clusters) by treatment arms
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Before
Initial sample size is approximately 60,000 shops. Half will be contacted using an account with a female name, half with a male's account name. 18.3% of shops will be contacted by a female customer using a standard script, 15.7% by a low-search cost female customer, 8% by a high-educated female and 8% by an uninformed female. Equivalent shares are assigned to each male-type combination.
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After
Half of the shops will be contacted using a female name and half of the shops with a male name
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