In all of the discussion about analyzing the best possible ways to intervene in global development, there’s an underlying component we really haven’t been giving enough attention to: Access to data for impact evaluation.
We still spend enormous resources on surveys, audits, and one-off M&E, only to discover months or years later that a program failed. By then, trust and funding are gone.
A more scalable alternative already exists in principle: administrative data. J-PAL and others have shown that reusing operational datasets (like tax records, health records, or credit score data) can make impact evaluation faster, cheaper, and more accurate. But in most LMIC contexts, access is limited and fragmented.
This is a highly neglected opportunity. Lowering the cost and lag of evaluation has massive RoI. Ineffective programs and deployments can be identified more quickly and cheaply. Successful ones can be scaled with confidence. It also makes continuous outcome tracking feasible. Imagine comparing typhoid trends across program cohorts in real time, using anonymized clinic data, rather than waiting for intermittent surveys.
This could also make it easier to popularize higher levels of rigor in M&E, if there were a way to couple data access partnerships with an accessible frontend.
There are real challenges (privacy, governance, logistics), but the upside is huge.
The focus, from the practitioner perspective, is supporting initiatives that will create these data sets, and establishing means to make operational use of them. I’m already exploring some angles to pursue this cause area and make lightweight comparison tests more accessible to the broader global health sector. Any thoughts on the value of this approach? If anyone in data science or software engineering has interest in collaborating, you’re also welcome to message me.
New Cause Area: Administrative Data Sets
In all of the discussion about analyzing the best possible ways to intervene in global development, there’s an underlying component we really haven’t been giving enough attention to: Access to data for impact evaluation.
We still spend enormous resources on surveys, audits, and one-off M&E, only to discover months or years later that a program failed. By then, trust and funding are gone.
A more scalable alternative already exists in principle: administrative data. J-PAL and others have shown that reusing operational datasets (like tax records, health records, or credit score data) can make impact evaluation faster, cheaper, and more accurate. But in most LMIC contexts, access is limited and fragmented.
This is a highly neglected opportunity. Lowering the cost and lag of evaluation has massive RoI. Ineffective programs and deployments can be identified more quickly and cheaply. Successful ones can be scaled with confidence. It also makes continuous outcome tracking feasible. Imagine comparing typhoid trends across program cohorts in real time, using anonymized clinic data, rather than waiting for intermittent surveys.
This could also make it easier to popularize higher levels of rigor in M&E, if there were a way to couple data access partnerships with an accessible frontend.
There are real challenges (privacy, governance, logistics), but the upside is huge.
The focus, from the practitioner perspective, is supporting initiatives that will create these data sets, and establishing means to make operational use of them. I’m already exploring some angles to pursue this cause area and make lightweight comparison tests more accessible to the broader global health sector. Any thoughts on the value of this approach? If anyone in data science or software engineering has interest in collaborating, you’re also welcome to message me.