GIE
Geospatial Impact Evaluations
Program Overview
Rigorous impact evaluations in a fraction of the time of an RCT
Geospatial Impact Evaluations (GIEs) rigorously evaluate the impacts and cost-effectiveness of specific development interventions and large investment portfolios with spatial data. GIEs methods leverage readily available data, like satellite observations or household surveys, to establish a reliable counterfactual for measuring impacts — at a fraction of the time and cost of a “traditional” randomized controlled trial (RCT).
Like RCTs, GIEs can estimate the net, attributal effect of a specific development program by comparing similar groups whose only difference was exposure to the program (or treatment). Unlike RCTs, GIEs use precise geographic data to establish this counterfactual retroactively, eliminating the need to assign program participants into randomized treatment and control groups.
Case Studies
Roads and Transportation
AidData and USAID/West Bank and Gaza have collaborated to conduct an evaluation of the Infrastructure Needs Program (INP) II, which funded the construction of water infrastructure, road networks, and schools with the ultimate goal of improving the Palestinian economy.
Natural Resource Concessions
AidData constructed a new dataset that measures the precise locations of 557 natural resource concessions granted to investors in Liberia, and then then merged these data with a remotely sensed measure of nighttime light growth at the 1km x 1km grid cell level to analyze effects across sectors and investor types.
Indigenous Land Rights
AidData studied whether formalization of indigenous communities' land rights affects the rate of deforestation in both the short and medium terms as the result of KfW-funded land tenure interventions across the Brazilian Amazon from 1997 to 2008.
Chinese Development and Sensitive Forests
What are the conservation impacts of Chinese development activities in ecological hotspots? We generate and sub-nationally geo-reference a dataset of official Chinese development activities implemented between 2000 and 2014 in the Tropical Andes, the Great Lakes region of Africa, and the Mekong Delta. We then merge these project data with a long series of high-resolution satellite data in order to evaluate their impacts on forest cover.
More Case Studies >
Benefits of GIE Methodology
GIEs can be implemented remotely and retrospectively, leveraging existing location-referenced data and generate portfolio-wide insights.
Features
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GIE Methodology
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)
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Partners
For partnerships and media queries, contact:
Alex Wooley
Director of Partnerships and Communications