OotuFund is pre-implementation. This page presents the rigorous global research our model is built on, our transparent fund allocation model, and our Phase 1 pilot targets for Laikipia County.
Key findings from peer-reviewed evaluations of cash transfer programmes worldwide.
Among UCT recipient households in Kenya RCT, vs control group.
Haushofer & Shapiro, American Economic Review, 2016 — GiveDirectly Kenya
Returned to local economy per dollar transferred in rural Kenya UCT programme.
Egger et al., Quarterly Journal of Economics, 2022
In Kenya CT-OVC programme households, with corresponding increase in school attendance.
Oxford Policy Management / UNICEF, Kenya CT-OVC Evaluation, 2012
Meta-analyses consistently find positive effects on consumption, health, and education outcomes.
J-PAL & IPA Policy Insight Reviews, 2022; World Bank Evidence Review, 2021
We publish our intended fund allocation model openly. The majority of every dollar raised goes directly to community members as cash — not administration or overhead. This model is subject to review as we approach implementation.
This is a planned allocation model, not an accounting of funds already spent. We will publish audited accounts once the pilot launches.
Independent evaluations from four programmes that inform OotuFund's design approach.
UCT · Kenya
A landmark RCT transferred ~$1,000 per household to 1,500 families in one of Kenya's poorest regions. At follow-up, recipients showed a 38% increase in food security, 31% rise in assets, and 58% improvement in psychological wellbeing. Consumption effects persisted 3 years after transfers ended, and local multiplier effects were documented at $2.60 per dollar transferred (Egger et al., QJE 2022).
"Cash transfers increased assets, reduced hunger, and improved psychological wellbeing — with no evidence of increased spending on alcohol or tobacco."— Haushofer & Shapiro, American Economic Review, 2016
Government Programme · Kenya
The Government of Kenya's Cash Transfer for Orphans and Vulnerable Children now reaches over 350,000 households nationally. Independent evaluation found a 42% reduction in child labour, a 51% increase in school attendance, and a doubling of household savings among recipients. The programme demonstrates government-scale UCT delivery is achievable with strong mobile money infrastructure.
"The CT-OVC programme produced substantial improvements in child welfare outcomes — including reductions in child labour and significant gains in school attendance."— Oxford Policy Management / UNICEF Evaluation, 2012
AI-Targeted · West Africa
In response to COVID-19, Togo's government partnered with GiveDirectly to deliver mobile money transfers to 500,000+ people. The programme used satellite imagery and machine learning to identify the poorest households. Evaluators found AI-based targeting was 4–11× more accurate at reaching the poorest than traditional community-based methods — with significant implications for how OotuFund designs its Kundi targeting system.
"Algorithmic targeting using mobile and satellite data substantially outperformed community-based targeting in reaching the most vulnerable households."— Aiken et al., Science, 2022
Basic Income · Southern Africa
In Otjivero-Omitara, all 930 community members received N$100/month for 13 months — unconditionally. Child malnutrition fell from 42% to 10%. School enrollment rose from 64% to 90%. Reported crime fell by 42%. Critically, local economic activity increased as cash circulated within the community rather than flowing out — demonstrating multiplier effects even at small-community scale.
"The BIG pilot produced rapid, measurable improvements in nutrition, education, and safety — while stimulating local economic activity through increased purchasing power."— Namibia BIG Coalition Evaluation Report, 2009
These are OotuFund's aspirational targets for our first pilot — subject to securing seed funding. They are not current achievements.
We are seeking seed and institutional grant funding to launch a rigorously evaluated Phase 1 pilot in Laikipia County. Every funder and research partner helps make this evidence base larger and more robust.