Hi Chioma, congratulations on successfully completing the challenge. While our team is bummed about missing out, the winners all have very interesting work.
I'm wondering if you can provide any feedback on where we went wrong? We didn't get much expert feedback and would love to iterate on both our current project idea for other grants, and understand IDEO's criteria better for future challenges.
Enjoyed the process a lot, and hope our paths cross again in the future.
Thank you Chioma, and thanks to the reviewers for their valuable feedback.
I will incorporate these responses into our final edits, but let me respond in detail here:
- We have already begun providing farmers crowdsourced information through the initial project described above. Since we provide farmers a list of service providers along with their quality rankings and contact information, they are able to use the information to either switch to better providers or put pressure on their existing provider to improve the quality of service provided. This has resulted in less wastage of the animal's productive lifespan, and of insemination inputs.
Interestingly, our rigorous evaluation using a Randomized Control Trial (RCT) demonstrated a 27% increase in successful pregnancies through Artificial Insemination, which translates to $32 in increased expected income for households living on roughly twice that per month. This increase came about primarily through the farmers' existing service providers improving the quality of their service in response to being confronted with this information.
- It is very important to us that our academic projects be policy-relevant. To date, we have worked with the Punjab Livestock and Dairy Development Department (LDDD) as our implementing partners. This is a public body that has the largest network of veterinary facilities in the region. We also have relationships with quasi-public and fully private veterinary organizations that have expressed an interest in working with us to implement this project elsewhere. Finally, we remain completely open to developing further partnerships with other implementing organizations.
- Ensuring that ratings are not falsified is a major challenge. In our original project, we faced this problem in two ways: first, the service providers sometimes attempted to send us positive feedback on behalf of service recipients. We dealt with this by verifying a percentage of the feedback directly with farmers, and bringing the malpractice to the attention of the service providers' superiors. Second, we also were provided overly positive or overly negative feedback by some farmers. This was a problem early in the implementation (when feedback was being collected but not disseminated) when we didn't have enough respondents per service provider, but the sheer number of farmers responding stabilized ratings before we started sharing the information onwards with other farmers.
Farmers are proactively provided the information, and they are also provided a helpline number they can contact if they would like more details at another time. The government and private sector entities also run helplines that this service could eventually be incorporated into.
Thank you for bringing Sidai Africa to our attention. Their model is very interesting, but somewhat distinct from ours. First, while Sidai focuses on building a network of franchisees and ensuring input quality through direct provision, we are focused more on enabling existing supply chains to improve quality by distinguishing good quality from bad, but do not directly supply product ourselves. Research has shown the value of both approaches, and both are necessary in a healthy input ecosystem. Second, we are not aware if Sidai has conducted a rigorous impact evaluation to measure impact. We have already conducted a randomized control trial meeting industry best-practices on the first component of our intervention, as described in detail in the attached research paper, and seek to do the same with the component proposed here to add to the knowledge pool on agricultural input quality.