Geospatial Impact Evaluations measure intended and unintended impacts of development programs. Leveraging readily available data like satellite observations or household surveys, GIE methods establish a reliable counterfactual to measure impact - at a fraction of the time and cost of a "traditional" randomized control trial (RCT).
Like RCTs, GIEs can estimate the net effect of a specific program by comparing similar units where the only difference was an intervention, 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 within the program design.
GIEs can be completed in a fraction of the time and financial cost of an RCT by eliminating the need for customized data collection in treatment and control groups before, during and after the program.
GIE methods are also flexible tools that can either be used to evaluate individual projects or project portfolios. Whereas RCTs are often implemented in narrowly bounded settings, GIEs can be used with data for an entire country (or even multiple countries), which makes it possible to draw conclusions about impacts and cost effectiveness that are broadly generalizable.
Additionally, GIEs can be implemented remotely, retrospectively, and affordably, opening up new opportunities to measure long-run programmatic impacts, which is especially useful to evaluators working in conflict and fragile state settings.
New research evaluating World Bank development projects, including those that pose the most environmental risks, finds no harm to important bird habitats.
AidData merged high-resolution nighttime lights imagery with precisely-located road improvements to discover how a $900-million investment impacted the Palestinian economy.
The project is designed to enhance the use of health data by the government of Côte d’Ivoire, civil society organizations and local communities.
Faster and cheaper than a randomized control trial but more rigorous than a performance evaluation, Geospatial Impact Evaluations (GIEs) fill the “missing middle” for organizational learning.
Ariel BenYishay, Rachel Trichler, Dan Runfola, Seth Goodman
Pablo Beramendi, Soomin Oh, Erik Wibbels
Maria Perrotta Berlin, Evelina Bonnier, Anders Olofsgård
Bruno Martorano, Laura Metzger, Marco Sanfilippo
Jonas B. Bunte, Harsh Desai, Kanio Gbala, Bradley C. Parks, Daniel Miller Runfola
Note: A version of this article was previously published as an AidData Working Paper.
Jianing Zhao, Daniel M. Runfola, Peter Kemper
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