Randomization Method
Randomization. Software-based randomization. Stratified randomization at the individual level.
Stratification. We stratify based on the five dimensions (each with two levels) below. The first four strata correspond to strata from the main experiment (AEARCTR-0017466):
• Gender (female yes / no)
• ISEE (household wealth status indicator with lower values indicating lower per-capita wealth) median split. Data from administrative records. Whenever ISEE is not reported (missing value; individuals can withhold this information but cannot claim welfare benefits if ISEE not reported), we treat these individuals as belonging to the above median group of individuals who have a non-missing value for the ISEE as most characteristics for the two groups overlap. This is to be expected as the incentive for reporting ISEE is higher for individuals with lower wealth.
• “Maturità” (final high school exam grade or its foreign equivalent) grade median split. Data from administrative records.
• BA/MA degree student. Data from administrative record.
• Progress: never watched any video / watched some video but didn't complete the course. Data from Moodle.
A small number of students have missing administrative date (due to late enrolment). We create an additional strata for these individuals.
Specifically, using Stata, we re-run the stratified randomization 20,000 times (seed 840801) and let the computer pick an outcome that produces the lowest treatment difference for baseline levels of (i) financial knowledge index, (ii) GAD-2 score, (iii) PHQ-2 score, and (iv) the average of all grades (from administrative data). We define the lowest treatment difference by summing up the absolute values for the four treatment differences and finding the lowest value. We then use this outcome for the final treatment assignment.