Hi! I’m Jess Littman, MSc in M&E candidate at American University and Evaluation Associate at Educate!, a social enterprise which works to prepare youth in Africa with the skills to succeed in today’s economy. We’re running a series of internal evaluations of our new distance learning models. These were piloted in Uganda, initially in response to COVID-19 and school closures, and are now growing into a scalable, sustainable way for thousands of youth to participate in remote skills training. The main vector for both youth participation and data collection is the mobile phone, and a major design and evaluation challenge so far has been the gender gap in mobile phone access.
Over the past year, we have seen a massive, rapid move towards remote data collection from all sectors of the evaluation field. While much of our focus as evaluators has been on getting the technology to work for our needs, issues of representation and participation in remote evaluations must not be overlooked. In Uganda, 17% more men own a mobile phone than women, and we suspect that the gender gap may be even greater for youth, Educate!’s target demographic.
The gender gap in mobile phone access is a challenge both in the design of remote programs and in how we evaluate them. We have found that women in our program more often rely on a borrowed phone to participate in distance learning, while men more often have their own phone. Not only can this discrepancy determine whether or not young girls have access to the curriculum and how often they can participate; it also increases the risk that they will be left out of our evaluation samples.
We have come up with several strategies to ensure young women are represented in our evaluations:
- Ensure that female enumerators call female participants. This reduces any perception of threat from the enumerator, either from the youth or their parents, increasing the chance of completing the interview.
- If the program isn’t balanced by gender, oversample women in the evaluation. This depends on your design – if you’re going for a sample which is purely representative of your overall population, this might not be the best approach. But for us, as we iterate on new programming, it’s more important to learn as much as we can about how the program impacts both young men and women, which means we need to maximize the number of both in the sample. This means we strive for a 50/50 balance in our evaluation sample. (This may change as we scale up the program and our learning questions change.)
- Collaborate with program implementers to improve gender balance. After an earlier evaluation found that phone access was a challenge for young women, we (the evaluation team) supported our program designers to target marketing at young women and to add a gender focus to our program retention strategy. Our next evaluation will look at the results of this update.
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