2026-27 AY PT Research Fellowship: Thirsting for Solutions
The Stanford King Center on Global Development’s Academic Year Part-Time Research Fellowship Program connects King Center faculty affiliates and affiliated researchers with undergraduate students committed to providing research support during autumn, winter, and spring quarters.
Students have the opportunity to engage in world-class research that has real-world impact. Undergraduate student research fellows are paid $19/hour*. Students must be enrolled full-time to participate and must be able to commit to research 8-10 hours per week.
*Students must attend orientation and submit an I-9 form to verify employment and receive payment. Students who cannot accept pay may be allowed to receive academic credit for this research.
Research Project Description:
This project studies how drinking water scarcity affects households in rural Sub-Saharan Africa. A central challenge is the lack of systematic data on well functionality. As a proof of concept, I address this measurement gap in Ethiopia by combining water-point observations with climate, hydrological, geological, and remotely sensed environmental data to train a machine-learning model that predicts well functionality across space and over time. I then link these predictions to household surveys to estimate the causal effects of well failure. I find that well failures increase school absenteeism. The results are not explained by health shocks or migration. Instead, well failures trigger a switch from groundwater to surface water leading to an increased water collection burden for children.
The proposed research will build on this work by scaling the approach across Sub-Saharan Africa in two steps. First, the goal is to extend the machine-learning framework to predict well functionality across countries and over time. This will provide a picture of how climate-induced drinking-water scarcity has evolved in the past and how it will evolve under different climate-change scenarios. Second, these predicted well failures will be linked to household surveys across Sub-Saharan Africa to test whether the effects documented in Ethiopia generalize to other settings and to study their broader consequences for health, school absenteeism, and the allocation of tasks within households.
Primary Research Mentor: Lucile Dehouck
Co-Research Mentor:
Stanford undergraduate students in good academic standing and enrolled full-time are eligible to apply. Co-term students must have undergraduate student status; if co-terms are in graduate billing status (after 12 quarters) they are ineligible to participate.
All majors are welcome!
Students Responsibilities:
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Primary:
- Extend well-failure predictions using future climate projections to assess how climate change affects the risk and spatial distribution of well failures.
- Clean and harmonize socio-economic surveys across Sub-Saharan Africa (LSMS and DHS).
- Causal inference on the impact of well failures on health, school absenteeism, allocation of tasks within households.
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Secondary:
- Test and compare different machine-learning models to predict well failures and improve predictive performance.
- Incorporate socioeconomic information such as population and wealth and assess whether they improve model performance.
Students qualifications:
- A background in environmental earth sciences, climate economics, computer science, or related fields is an advantage.
- Familiarity with Python/R and Stata programming and experience handling large-scale spatial datasets would be helpful but are not required.
- Most importantly, the student should be curious, motivated, and eager to learn about the impacts of climate change and water scarcity on rural households, as well as how to manage and analyze large-scale geospatial data.
Time Commitment:
The time commitment is 8-10 hours per week (equivalent to a 3-unit course) each academic quarter. The expectation is that students will work the full academic year with their mentor (Autumn, Winter, and Spring quarters). Students planning on studying abroad may not be eligible.
To Apply:
Along with the application, applicants are asked to submit:
- a cover letter
- resume or CV
- unofficial Stanford transcript (first quarter frosh do not need to submit transcripts for autumn quarter applications)
Research Mentor Questions for Applicants:
- Do you have experience coding in Stata?
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Do you have experience working with geospatial data?
- If so, please describe the types of spatial data, software, or methods you have used (e.g., GIS, raster or vector data, remote sensing).
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Do you have experience coding in R or Python, particularly for data analysis or machine learning?
- If so, please describe your experience and any relevant coursework, research projects, or machine-learning methods you have used.
