Hi Habiba,

Thanks for your answer! It is really useful to know your opinion, the more real world cases we can base our decisions on the better for everyone. And you are right, the long-haul is not always the ideal.

Im really happy you found what is best for your org and that you can continue your great work!

Thanks John for the answer, I actually appreciate it a lot, not unsolicited at all!

Will reach out to you directly, you are definitely someone we would love to hear about this topic. Thanks again!

Habiba incredible work you have been doing, massive congratulations and I hope you fundraise all the money you need!

I have one question, if you can share publicly. It seems that the organization is challenging enough for two co-founders, and as you mentioned being a sole CEO has its risks. What do you think could have prevented the other co-founder from leaving so soon?

As we run an incubation program, I am particularly interested as we are trying to build partnerships that are really exciting and fulfilling for all co-founders, but not being naive about the real day-to-day challenges of co-building, and maintaining momentum when there are so many other exciting and important things to also do in the world.

Thanks for writing the post, really insightful!

Hola Nat,

Gracias por tu pregunta y disculpa que recién lo veo.

GiveWell tiene el mandato de donar a las organizaciones que con evaluaciones rigurosas verifiquen el mayor impacto por dólar y que sean las más costo-efectivas en sus áreas priorizadas (principalmente salud global). África al tener un costo de vida más bajo que cualquier otra región y al tener una escala mayor de los problemas, sin dudar, debe ser el lugar priorizado. 

Dicho esto, eso no implica que paralelamente en muchos otro lugares no deje de existir la necesidad de organizaciones más costo-efectivas. Así que respondiendo a tu pregunta, aunque no creo que en LATAM lleguemos a los niveles de costo-efectividad de las Top GiveWell orgs (quizá me equivoque), sí creo que el sector en la región tiene mucho potencial de mejora. Por ejemplo en 1) priorización y enfoque de problemáticas (las orgs hacen mucho al mismo tiempo, perdiendo el foco y por tanto eficiencia), 2) en usar evidencia para tomar decisiones (tanto externa, para aprender sobre que ya ha funcionado en otros contextos, e interna, llevando procesos serios de monitoreo y evaluación); y 3) con un diseño y objetivo de escalar, por ejemplo, a través de los gobiernos, para que las iniciativas no se queden en los miles, sino tengan la opción de llegar a los millones que requieren las soluciones.

El mundo es amplio y hay muchas personas en la región listas para crear y trabajar en estas organizaciones, y muchas organizaciones/personas ya donando o que podrían donar más efectivamente, así que las comparaciones son útiles para ver lo que es posible, inspirarnos, aprender y trabajar.

Gracias por el apoyo, si tienen interés y posibilidad de apoyar a las orgs del cohorte, estamos ahora creando nuestro círculo de donantes y toda ayuda suma: https://www.emprendimientosocial.org/efectus-mariposia 

Gracias!

Awesome! Will do, thank you!!!

Thanks Vasco!!!

Thanks for the comment Marysabel; so much still needs to happen, and we know there are so many committed people (like yourself) willing to take on the challenge!

Thanks Patrick, very grateful for all the support!!!

Thanks Vasco, hugely appreciated! 

Hi Vasco,

Thanks for the question, happy to answer.

The CEAs were built with different levels of involvement from the fellows, our team, and LLM support. Claude being the main supporting tool.

The template and overall structure of the CEAs were developed outside of Claude, based on our methodology (and heavily comparing different templates from different organizations). 

As for the content of the models:

• Costs: fellows first analyzed the cost structure of the interventions they were drawing evidence from (i.e., understanding the key cost drivers). Based on that structure, they built localized budgets using country-specific prices and costs. All organizational budgets were developed by the fellows within our template for direct implementation, with those benchmarks and certain assumptions, we calculated the intervention cost at scale, so no Claude involvement here.
• Key assumptions: (e.g., reach, limiting factors) were fully defined by the teams, based on their contextual knowledge, and we then used scale factors, to project reach at scale, so no Claude involvement here.
• Evidence: (RCTs, meta-analyses, etc.) was reviewed as part of the research process (we used other LLMs for this as well, mainly Elicit and Perplexity); Claude mainly helped structure and organize the information within the template.
• Calculations: were done with Claude, but step by step, so we reviewed every output generated (making this the most time-consuming part of the process). I can confidently say we reviewed every cell, adjusting assumptions, asking questions, and catching errors. Of course, this is also the part where errors are most likely to occur, and these are only the first versions of the models, so I am also confident that there are still mistakes we did not catch.

Once the models were completed, we held review sessions with each team to revisit key assumptions and refine the models. For example, asking whether certain implied assumptions made sense for the real world, and because the fellows themselves know their context and intervention details, many things were changed. However, the models are far from being final, but the template structure was created for them to be used as a planning tool (not only a fundraising tool), so teams will definitely adjust them with time and with their internal evidence, once available.

In full honesty, building the models was the part of the incubation process that scared me the most, so I’m very glad Claude appeared at the right time, and it also made me more confident in that using templates like this is a way to make CEAs more accessible: once organizations have clear cost structures (and many orgs have detailed budgets), assumptions, and external evidence, building a first model becomes much more feasible. It’s not perfect, but it’s a strong starting point.

Very happy to share the template or walk through it together, we’re very keen to improve it and learn further with feedback.