The Impact of Child-Optimized Financial Education (COFE) Curriculum in Uganda

Child-optimized Financial Education (COFE) curriculum was designed by Catholic Relief Services to be delivered by a Savings and Internal Lending Community (SILC) private service provider. COFE is a financial education curriculum designed to help caregivers make informed, child and HIV-sensitive financial decisions. The curriculum weaves in themes around child protection and reducing HIV stigma and discrimination and promoting HIV testing, treatment and retention in care (especially the financial components). In addition, COFE was designed to help caregivers: 1) make financial goals that will help them provide for the needs of all the children in their care; 2) make good financial choices about income, expenses, budgeting, savings and borrowing; 3) learn basic money management skills, including saving for emergencies; 4) apply those skills to increase income to better invest in all the children in their care; 5) create a succession plan and a written will; 6) applies learning from the field exercises to ensure proper nutrition and healthcare for all children in their care; 7) complement other project activities and trainings by reinforcing messages and behaviors related to good parenting, child protection, and HIV prevention, care and treatment; 8) ensure that financial challenges and the family's need to earn income do not put children and adolescents in harm's way. There are 13 COFE sessions of 45-60 minutes each that are delivered every other week by a private service provider (PSP) at their regular SILC group meeting day and time.

2020-09-01

2021-03-31

Whether caregivers paid for required education expenses of all the school-age children under the caregivers' care to stay in school

Required education expenses to stay in school can include tuition fees, school uniform costs, school supplies and transportation to school among others. School age is defined as ages 6+ and only those whose schools were not closed due to COVID were counted.

Whether caregivers paid for health-related expenses of all the children under the caregivers' care; caregivers' financial self-efficacy

Financial self-efficacy measures confidence in budgeting, borrowing, problem-solving and making progress towards financial goals.

The present study utilized a post-test only randomized controlled trial design. Specifically, due to the COVID-19 epidemic, we were unable to ascertain baseline outcome data for study participants. This design should minimize confounding of the treatment effect due to participant-level and cluster-level characteristics; however, the lack of baseline measurement means that we cannot estimate changes in the outcomes over time, or assess whether any changes over time were differential by treatment status. Existing pre-test programmatic data collected by Catholic Relief Services will be linked with the post-test survey data to supplement additional background information for participants.

We selected 28 Private Service Providers (PSPs) and 3 SILC groups per PSP. We then randomly allocated the 28 PSPs (in a computer program) to one of the two arms of the CRT: that is, 14 PSPs were allocated to COFE+SILC and 14 PSPs randomly allocated to SILC alone. The 3 SILC groups within each PSP were therefore allocated to the arm to which that PSP had been allocated.

Regarding the selection of SILC groups within each PSP, we first subset down to only SILC groups with at least 33% of the member households with orphans and vulnerable children (OVC), except when a PSP had fewer than 3 groups with at least 33% OVC. Then we sorted by SILC cycle within each PSP, preferring SILC groups at cycle 1, and kept the three SILC groups with the three largest proportions of OVC at the earliest cycle. Finally, to subset down to 28 PSPs (out of 29 available), we kept the 28 PSPs with the largest number of OVC members.

We then used a constrained randomization routine to allocate the selected 28 PSPs to the two arms of the trial. Our goal was to balance the two arms on important PSP-level characteristics hypothesized to be related to the financial outcomes of interest so as to avoid confounding. We note that randomization is expected to achieve this “balancing” (i.e. to avoid confounding) when a large number of PSPs are randomized but because CRTs usually randomize a small number of clusters (i.e. fewer than 40 total clusters), it is important to use a strategy to help ensure balance is achieved. In this way, we would expect that the “Table 1” for this trial would show comparable characteristics between the COFE+SILC arm and the SILC arm.

The four PSP-level characteristics chosen for balancing were: proportion of group members that are OVC; proportion of group members that are using Edufund (i.e. a shared fund that a SILC group could implement to focus on paying school expenses); PSP experience in months (i.e., months since their first training); and region (Gomba vs. Mityana).

We gave greater weight to OVC and Edufund, since these were determined to be most important of the four. This ensures that these will be the most balanced of the four variables.

In the constrained randomization routine, we used a constrained randomization space cutoff of 0.1, and simulated 50,000 randomization schemes (since the total number of randomization schemes with 28 clusters is >40,000,000). We checked randomization validity, and found acceptable validity given that “a reasonable constrained space should ensure each cluster pair appears in the same arm (and in different arms) for at least 25% of times and at most 75% times”. It fell slightly outside of this range, but not by much (19%).

The unit of randomization is the private service provider.

Yes

28 private service providers (with 3 SILC groups per PSP).

An estimated 1600 participants who are enrolled in SILC groups will be interviewed, roughly split between the intervention and control arms. Twenty-eight PSPs were constrained randomized 1:1 into intervention (COFE + SILC) or control (SILC only). Each PSP had 3 SILC groups selected for participation in the study. After enrollment, it was determined that only 1600 participants could be surveyed. Hence, 10 SILC groups were dropped (stratified by treatment arm and constrained to only drop one group per PSP max) for a total of 74 groups.

14 private service providers in the intervention group and 14 private service providers in the control group.

We powered on the difference between intervention and control arms for the primary outcomes, ability to pay for school expenses for a child. Power calculations were performed in Optimal Design Software, with the following assumptions partly based on preliminary data: • Two-tailed type 1 error of 0.05 • ICC at the PSP level < 0.0001 • ICC at the SILC group level: 0.06 • Standardized effect sizes = 0.35 • The number of SILC groups per PSP = 3 • Average number of participants per SILC group: 23 • Proportion of participants having at least one child in their care with school expenses =70% With a total of 1932 participants (28 PSPs, 3 SILC groups per PSP, 23 pp per SILC group), we have 88% power to detect a standardized effect of 0.35. However, we could only fund surveys for up to 1600 people. As a result, we randomly dropped the number of SILC groups required to reduce the total sample size to or below 1600 participants, stratified by treatment arm and constrained to only drop one group per PSP max. With a minimum of 1288 participants (28 PSPs, 2 SILC groups per PSP), we have 84% power to detect a standardized effect of 0.35. This puts our final estimated power between 84% and 88%.