Catalyzing Next Generation Impact Evaluations
Geospatial Impact Evaluation (GIE) methodologies have continuously improved since AidData launched the original GIE Primer. In 2022, our GeoQuery tool surpassed 20,000 requests for custom geospatial data extractions. Together with DevGlobal, Mercy Corps, and the Bill & Melinda Gates Foundation, we started the GeoField initiative to make impact evaluations and climate sensitive agriculture more effective through Earth observation. To gain an introduction to the state of the art in impact evaluations, check out our online course, Toolkit for Agricultural Geospatial Impact Evaluations.
Benefits of GIE Methodology
GIEs can be implemented remotely and retrospectively, leveraging existing location-referenced data and generate portfolio-wide insights.
Learn more in our #CGDTalks Video
Geospatial impact evaluations (GIEs) rely on subnationally geo-referenced intervention, outcome, and covariate data and quasi-experimental methods of causal inference to measure the intended (or unintended) impacts of development programs.
GIEs seek to mimic the conditions of a randomized control trial (RCT) with observational data. RCTs are powerful because they create conditions under which one can reliably ascertain that individuals’ participation in a program was not correlated with their outcomes. GIEs create similar conditions, but without randomly assigning individuals to treatment and control groups.
For our in-depth exploration of GIEs, please see A Primer on Geospatial Impact Evaluations Methods, Tools, and Impact Evaluations.
Using counterfactuals to find impact
The key is making comparisons across individuals who are sufficiently similar to one another and experiencing changes that are otherwise similar. The best way to make such comparisons is to identify comparison individuals who are geographically close to the program participants, but unlikely to be affected by the program’s presence. Doing so requires geographically precise data on programmatic interventions and their intended (or unintended) outcomes. Such data are rapidly expanding in number, scope, periodicity, and availability.
Rather than using randomization to identify counterfactual cases, GIEs seek to achieve a similar result through one of three methods:
- Strategic subsampling of observational data to identify treatment and control cases that are nearly identical but for the presence or absence of the intervention (e.g. propensity score matching)
- Comparing the pre- and post-intervention change in the outcome of interest for a treatment group relative to a control group (e.g. difference-in-differences, fixed effects)
- Exploiting the discontinuity around a geographic cutoff that is “as-if random” (where the treated cases and control cases on either side of the cutoff are extremely similar across pretreatment covariates)
Does Irrigation Strengthen Climate Resilience? A Geospatial Impact Evaluation of Interventions in Mali
Ariel BenYishay, Seth Goodman, Rachel Sayers, Kunwar Singh, Madeleine Walker, Mascha Rauschenbach, Martin Noltze
Landmine Clearance and Economic Development: Evidence from Nighttime Lights, Multispectral Satellite Imagery, and Conflict Events in Afghanistan
Christian Baehr, Ariel BenYishay, Rachel Sayers, Kunwar Singh, Madeleine Walker
Break it down: A disaggregated analysis of the effects of aid on stunting
Dick Durevall, Ann-Sofie Isaksson
The effects of trade, aid, and investment on China's image in Latin America
Vera Z. Eichenauer, Andreas Fuchs, Lutz Brückner
Linking Local Infrastructure Development and Deforestation: Evidence from Satellite and Administrative Data
Christian Baehr, Ariel BenYishay, Bradley Parks