third the length, and one enumerator did the work of a team of three with the monitoring tools, the QAS tools were roughly a third of the staff costs to implement as the diagnostic research tools (see appendix 2). Given the relative consistency between data collection firm and MoETE supervisors, who were randomly assigned, one important implication of our results is that existing staff may well be able to undertake QAS data collection; although there may be some opportunity cost in shifting their time allocation to the QAS, this may make implementation and eventual scale-up more feasible.

While ministry staff can be used for cost savings, other aspects of the QAS system may merit more investment. Longer observation times, for example, may lead to more consistent results. Ongoing efforts to validate and improve question and response design may make the QAS more informative. Increasing the length of training, as well as the quality of training are important areas that may improve the quality of the QAS data itself. Which of these improve consistency and accuracy most cost-effectively is an important question that merits further research. One challenge in Egypt was that we were unable to obtain local videos of teaching in classrooms, due to security and privacy concerns, so enumerators trained on international examples. Investing in local training materials may help improve the functioning of the QAS. The enumerators all did pass reliability quizzes (80% correct or above), but this may not be sufficient to ensure high reliability during fieldwork.

Collecting a full census of QAS tool data from all schools on a regular basis can also be valuable, but may be costly, and resulting data should be used with caution when informing quality enhancement actions. QAS tools should be low-stakes, not high-stakes, and treated as formative not summative. There can sometimes be positive potential information/incentive effects of quality information being released, as with school report cards (Aturupane et al. 2014; Andrabi, Das, and Khwaja 2017; Bassok, Dee, and Latham 2019). We recommend against using QAS results for sanctions or rewards on a teacher level (raises, promotion, bonuses, retention, or firing), given their imprecision. Likewise, we recommend against rewards or sanctions on a school level for performance. Recognition (e.g., certificates, banners) for top performing teachers or schools may be inaccurate but also may incentivize quality (Cotofan 2021).

When governments lack the resources to provide interventions universally, the QAS can help target interventions. Although imprecise, the information in the monitoring tool is nonetheless an upgrade from not knowing how to target at all. For some interventions, such as hiring additional teachers to reduce class size, other systems (e.g., enrollment and staffing data) may allow equally good or better targeting. For other interventions, such as reading tutors or literacy training, knowing which schools have more children struggling with literacy can be helpful for targeting. QAS results may be too imprecise to accurately target professional development or coaching for teachers. Yet, they could act as a starting point for activities such as coaching (which then includes subsequent observations as well, providing further data on quality). However, implementing tailored coaching at scale can also be quite difficult.

In terms of QAS design, while children’s skills are naturally variable, they do seem to be one of the better-measured items in QAS. Especially since links between observed quality and child outcomes have been found to be weak in QRIS (Zellman et al. 2008; Elicker et al. 2011; Tout et al. 2011; Keys et al. 2013), directly including standards and monitoring children’s skills is also 24