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The use of social norms on the payment of University tuition fees

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modified on 2017-05-07, 19:26
The use of social norms has become the preferred tool of choice for behaviourally informed interventions. However, it is still not clear in what type of contexts and populations is this type of interventions effective. This randomized controlled trial with 4298 students tests the applicability of social norms to improve the late payment of university tuition fees.

The study was designed and carried out at University College London (UCL), with the assistance of the Student Fees Office. The project was approved by UCL Ethics Committee (ID:3949/004).

The Fees Office sends out a reminder email to the students who have not paid their fees on time. The intervention kept the existing wording of the reminder email but inserted the sentence in bold “OVER 90% OF UCL STUDENTS HAVE ALREADY PAID. PLEASE PAY THE AMOUNT DUE NOW.” which appeared in a prominent place near the top of the email (see Appendix for treatment and control email). We ran the experiment over two years with the Fees Office sending email twice each year in November and February resulting in four rounds of emails. The control was the normal email, but adjusted so that the only different between the treatment and control was the insertion of the new text. The allocation of treatment was randomised and blocked by age and gender to ensure balanced samples. The original sample includes a total of 4374 emails sent, from which 76 emails were removed from individuals who were in debt twice over the two-year period of the study, and as result received two emails (we removed the 2nd email). The final sample includes 4298 individuals who were sent 812 emails in November 2013, 1459 emails in February 2014, 652 emails in November 2014 and 1375 emails in February 2015.

We use logistic regression models, with a binary outcome measure of payment by 14 days of reminder being sent, to estimate the effect of the intervention. We first use a univariate model with only the treatment predictor and then use a multivariate model including covariates for gender, age, initial amount of debt, year (2013/2014 or 2014/2015) and round of reminders (November or February).