Optimal Design to Motivate Blood Donation

We send different versions of mobile message to past blood donors, encouraging them to donate whole blood at a local bloodmobile. Subjects that show up at a local bloodmobile are also requested to fill out a survey.

2014-12-20

2015-01-04

We focus on three behavioral outcomes: (1) whether a subject comes to a bloodmobile to donate blood; (2) where the subject brings friend(s) to donate together; and (3) the amount of blood donated by the subject and his/her friend(s). We are also interested in how subjects perceive the cost of donation and how communicate blood donation experience with friends, as reflected in the survey questionnaire.

We started by choosing participants from past donors of the blood bank based on three criteria: first, the blood donated by the particular donor must pass a battery of blood test, which is important because the bank aims to increase supply of qualified blood; second, the donor has not donated in the last six months, as a 1998 nationwide law disallows any donor from donating whole blood twice within six months; third, the donor has made at least one donation in the past 25 months. Condition on 80,000 participants that satisfy these criteria, we randomly assign them into seven test groups. The first one is the control group with 14,000 subjects who received no message from the blood bank. The remaining six groups (with 11,000 subjects in each) received different mobile messages.

We started by choosing participants from past donors of the blood bank based on three criteria: first, the blood donated by the particular donor must pass a battery of blood test, which is important because the bank aims to increase supply of qualified blood; second, the donor has not donated in the last six months, as a 1998 nationwide law disallows any donor from donating whole blood twice within six months; third, the donor has made at least one donation in the past 25 months. Condition on 80,000 participants that satisfy these criteria, we randomly assign them into seven test groups. The first one is the control group with 14,000 subjects who received no message from the blood bank. The remaining six groups (with 11,000 subjects in each) received different mobile messages.

In particular, message 1 only reminded subjects to donate, message 2 added an explicit reward for donation (a supermarket voucher that is worth 30-50 RMB). The average daily wage in this city in 2014 was about 100 RMB, so the reward amount is non-trivial. Neither message 1 nor 2 mentions group donation. In message 3, we reminded the subject to donate with friend(s), but did not mention the economic reward for donation; message 4 included both a reminder for donating with friend(s) and the economic reward. Note that in both message 2 and message 4, the reward is presented as reward per donor, without any condition on whether the donor comes alone or with friend(s). Message 5 is similar to message 4, except that we made the reward conditional on donating with friends (“…if you and your friend(s) donate together, each one of you will get a reward of…”). Message 6 is similar to message 4, but highlighted additional gifts available for all donors that come in group (“… you will get a reward of … upon donation. If you and your friend(s) donate together, each one of you will get an additional gift.”).

Randomization done in office by a computer.

Randomization is done by individual.

No

Total 80,000 individuals. No clustering.

80,000.

Control group: 14,000 individuals.
Treatment group 1-6: 11,000 individuals each.
Total: 80,000 individuals.

The existing literature suggests a response rate around 1%. Under the null hypothesis of response rate equal to 0%, we compute that a 1% response rate will generate a t-statistics of 10.5 if the sample size is 11000. Under the null hypothesis of response rate equal to 0.5%, we compute that a 1% response rate will generate a t-statistics of 5.27 if the sample size is 11000. If we compare two samples with response rate 1% and 2%, the t-statistics of their mean comparison will be 6.11 if the sample size is 11000 in both samples.