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MUNA NDULO: Andrew Mude really needs no introduction to Cornell. He did his PhD here in the Economics Department. I did not have the pleasure of having him in one of my classes, but I know that he was good friends of many of my colleagues and researchers and grad students, especially Ed [? Mumbai ?] sitting there.
I can imagine them spending many hours studying together, and perhaps they engaged in drinking some beer. But interesting that Andrew has, in a very short period, I would characterize as a rising star. He has had phenomenal contributions early in his career to economic development, and I'm very, very proud of him and the work that he's done.
Mude reminded me that Andrew was an IAD Fellow, and this is an investment that Cornell has made. And we look upon our graduates with pride and hope that they do well. And in this case, he's done extremely well. These types of investments, it must continue, and I wish our university would really do so. I think this is a living example of what wise investments can do.
Andrew is a principal economist with the International Livestock Research Institute. And there's a little secret among development practitioners-- Rodney-- is that if you really want to do really difficult, hard work in development, you work with livestock. These animals are mobile, they're nomadic, they don't really look across national boundaries.
They really haul the stock. They are almost banks themselves. It's hard to look at how you affect development with such a fluid set of assets roaming around the countryside. But Andrew has really-- equipped with a PhD in economics-- have taken this to heart and has done some very innovative work in expanding risks and inclusiveness for this part of the sector. Indeed, building resilience is a term that I sometimes avoid.
But he has done really pioneering work here. He has won a Borlaug Award, numerous publications in this particular area, for his pioneering work of finding an index. Insurance work so farmers could reduce their risk in this part of the economy.
So with that said, ladies and gentleman-- I can go on and on about Andrew. I know he spent some time at Harvard University as a fellow. He's won the Borlaug Award for Young Scholars for this year, which is the highest award for a person under 40 years old in international development. I'm delighted and pleased and proud to present Andrew Mude. Andrew?
ANDREW MUDE: Thanks. Thanks so much for that great introduction, Professor Christy. It's always great to be back in Cornell. I graduated in 2006, but I try and come back at least once a year through our collaborative work agreement that I have with my advisor, Professor Chris Barrett. But I've never had the opportunity to come back and engage with the IAD, and see friends like Professor [INAUDIBLE]. It's great to be here. Thanks for the invitation.
And Professor Dune. I'll admit that when I was invited, of course I said, yes, I'll come. And when I was told-- when I was asked to be the keynote speaker, I stalled a bit. Because I must admit, because it will become very apparent shortly, I don't actually work in the-- or my research is not really in the field of mobile money. So what am I doing here, exactly?
Well, I do development in Africa. And I don't know, Florence, if you'll accept the definition of financial inclusion, but I think that I can say that having worked, as Professor Christy said, worked to design and develop and scale up on insurance, so a financial product, to the previously uninsured, you could say I work in financial inclusion.
M-Pesa was started in Kenya, and I'm Kenyan. I don't know if that gives me bonus points for mobile money. But I don't know, Professor Christy mentioned the Borlaug Award. Maybe I can write Borlaug's tale. Because Norman Borlaug, he agronomist, received the Nobel Peace Prize in 1970.
Well, he received it for the great work he did in developing drought-resistant, high-yielding wheat. Not just developing, but he would say most of his work was in ensuring the adoption of the wheat in Mexico, in India, in Pakistan. And he received the Nobel Peace Prize because they say a hungry man is an angry man. And he received it because of having saved billions of lives from starvation.
But Borlaug would always say that-- despite this achievement, he would say that the greatest, the most important factor for our rural agricultural development would be roads. And he always maintained that across his life. But toward the end of his life, his final decade-- he passed on in 2009-- he started saying something else.
And he started saying that actually, ICT, Information and Communication Technology-- that's the word you use-- is the single most important thing for rural development, to enhance and catalyze rural development right now. And ICT is almost like roads. You can think of modern day infrastructure, vehicle, exchange of ideas and goods and so on.
And well, Borlaug was a visionary. And I can't claim the same when it comes to mobile, but my program now does a lot of work with regards to mobile technology. Perhaps not mobile money. And so I figured since this conference is going to be talking a lot about mobile money, and I [INAUDIBLE] that he also looked at a different aspect of mobile in the provision of input subsidies, which is another very important part of the puzzle for rural agriculture development.
There is a lot that-- the power of mobile is so much more than finance. And while some might say-- or money. And money might be king. There'll be a lot of talk about mobile money. So I thought I would highlight some of the other benefits of mobile technology.
I'll do it because my team and I came through or stumbled upon the importance of mobile as we tried to identify solutions to a range of problems that we kept coming against when we were trying to push this new technology to a population that had never heard of insurance, and in an area that, as Professor Christy had said, is not low-hanging fruit.
And mobile kept coming up time and again as a solution. And so to set the context, let me start with a bit about the program that we do and the elements in which eventually a suite of mobile technologies helped us begin to catalyze a process toward broader adoption and scale.
So the work is an index-based livestock insurance program. It's basically a financial solution. And the target area is the dry lands. And so dry lands in the country of Kenya, where I live, at least 65% of the country is designated as dry land, arid land. And in these areas, extensive livestock keeping is really the only production system, or at least the main production system in the area.
And the same is true of the Horn of Africa, other source of Africa and the Sahel, and so on. So this is the area that we chose to focus. Now recently, as a result of the increase of our mandate beyond insurance and to mobile technologies and so on, my team and I sat down about two months ago, and we worked to try and reorient our focus.
Our mission. So we changed our mission statement. We have it here developing innovative and evidence-based solutions and trying to support the adoption for livestock keepers in the drylands. And we expanded two problems. Because previously, we had been focused on this problem, which is a recognition that in these dryland areas, agroclimatic vulnerability, vulnerability to drought of their key asset is one of the most important problems.
And so that index-based livestock insurance is part of a strategic effort to deal with that problem. And over time we realized there's another particularly important problem that this population faces, and that is a problem of information scarcity. And information scarcity is important, is a constraint in many parts of rural Africa.
But in the type of areas that we work, in the drylands, these are areas that are even more remote. They're even more infrastructure deficient. So things like roads, access to mobile networks and so on, are a lot less. And so the price of information and the benefit, therefore, that information can confer. Information regarding opportunities. Information regarding educational options or understanding what values of a range of options might be become very important.
So this is now what we work on. But how did we come about it? Let me try and set the context. I mentioned that the population of the target is the drylands. And in the drylands, pastoralists. These are extensive livestock keepers, are the key habitant and is the key productive system.
And it's quite a sizable constituent, over 50 million of them in sub-Saharan Africa. 20 in the Horn of Africa. About 4 million in Kenya. And for them, livestock is life and livelihood. So the median household has all of its productive assets in livestock, and about 40% of household income also derive from livestock products.
And in these environments, and for these households, drought is the biggest source of risk. I mean, drought, you've heard of, perhaps, the boom and bust cycle. Some of you might be hearing right now about the famine that is going on in the Horn of Africa and other areas.
But famine is usually-- these are the populations that are most vulnerable to famine. And by the time it gets to famine, famine is talking about threats to human life. But before it gets to that is threats to their productive asset. And when you lose your productive asset, basically, you're threatening the stream of your livelihood for the next several years.
So we try to figure out, how can we deal with this problem. And like I mentioned, when you lose your livestock, you're basically losing your asset base. So you're affected through time. And because of this risk, you're also-- your incentive to invest in this asset, this only asset in your productive system, lowers.
And when drought hits, the responses that most governments-- you've heard of food aid. There's the stocking. These are expensive. They are reactive. And so we looked for another solution, and we came upon insurance.
Insurance is used in most places to help manage the risk of asset loss. But typical insurance is not suitable in these areas. So we stumbled on this new technology in insurance, index-based livestock insurance. Basically, you're not insuring an individual's livestock, you're insuring an index that is associated with the risk they face.
And so in this case, we used satellite data, which is freely available, high resolution data across time and space, and gives you an indication of the vegetative vigor, or the level of greenness, which basically relates to how much these systems-- because these livestock depend fully on the range lands-- how much nourishment there exists for them. And you can then exploit this data to development models and write insurance contracts against the risk of drought.
And so this is what we have been working on since we launched in 2010. Well, just before 2010, doing the design, bringing together the coalition of partners, insurance companies. 2010 is when we had our first pilot in a small county in Kenya. And since then, there's been a lot of momentum to scale. The government of Kenya had taken it up. It's scaled across seven districts in northern Kenya.
The recent drought was one of its big milestones, where about 12,000 households were paid about $2 million. This was about 90% of the covered households under the Kenyan program. We also have a program started off in Kenya and southern Ethiopia 2012, and now the World Food Program has taken it up as part of its basket of response in these areas, including food aid and other things that it does.
So this is the implemented program. And there are five key components that over time we have identified as basically the components of a sustainable index-insurance program. And as these program scale, and we get demand from other countries in the Sahel and Southern Africa to help them develop these products, these are the five areas that we focus on.
And I'll just go through them briefly and then also mention key problems that we faced in these, for which mobile has helped us solve or is increasing the efficiency with which we deal with these challenges. So first is contract design. Any insurance program rests only on the precision of its contract.
And so first off, when we started, we were insuring against drought-related livestock mortality. So you can think of this as life insurance for livestock. An asset replacement contract. And to do that, we started in the area in Kenya, in Marsabit County, which had the best quality data to allow us to design these contracts. And that is livestock mortality data.
So we had livestock mortality data since 2000, every month, a sufficient number of households. And we are pairing this with remotely sensed data I mentioned. Normalized difference vegetation index. And you pair these together, and you create an empirical relationship that gives you predicted area livestock mortality.
And so now the insurance companies no longer-- they don't have to go and verify that this is very costly, and this is the benefit of insurance. Instead, they just take the freely available remotely sensed data, plug it into this response function, and you get area average mortality. If mortality drops below a certain amount, you get paid. Right?
That's great. It worked very well. 2011 there was a first drought in Kenya. The insurance paid off. People got excited. They wanted to scale up.
The problem with scaling up is we started in Marsabit because the data was very good. So as you start to scale up, you're entering places where the data is not as good. And so your response function is not predicting mortality as well.
And so we said, let's try it anyway, and we wrote papers around it, and so on. But very quickly, there started to be questions. Because sometimes the insurance was paying off more than it should. Other times it was not paying off when it should pay.
So we had to rethink. And in that rethinking, we then moved to a new type of contract, which is the contract that is currently being sold or offered in Northern Kenya and Southern Ethiopia. And this is basically a seasonal forage availability index. So it relies exclusively on the remotely sensed data.
And you can see in this-- does it work? Well, you can see the graphic there on the lower right, basically showing you how we go about aggregating and trying to map the data into that risk profile. But this has some problems.
And the problem with this is it's working actually quite well. Very easy to scale. And actually, pastoralists and the target client prefer this, because even with the livestock mortality contract, this actually-- I failed to say that this then allowed us to move from asset replacement to an asset protection contract. So you can think of this as health insurance for livestock.
And this was in response to a question, why do you have to wait until our livestock die? Can you intervene in advance? And so this type of contract allowed us to do that.
But the problem with this is the remotely sensed data is only so precise. It tells you the amount of greenness, but pastoralists will ask you, there is some green that is not very palatable, particularly in areas where you have invasive species. So we can control for some of that, but we can't control for it perfectly. Mobile phones, I'll demonstrate how mobile phones is helping us to solve this problem.
Another is the standard of researchers. We had 925 households in Northern Kenya when we started baseline. Followed the program through six years. Southern Ethiopia, 515 households.
And basically what we were trying to do is establish if insurance had impact at the household level. And we have other range of research efforts to look at the value for money at the aggregate level. So this was great. We published some papers. We got some significant impacts at the household levels.
Households who had insurance were less likely to reduce nutritional intake for children under five during times of drought stress. Households with insurance were less likely to engage in distress sales of their livestock. So when the season returned, they had faster growth. Insurance was related to higher milk productivity, and so on and so forth.
So a lot of benefits. And we took this information to the government, and this is what convinced the government to take up the product. Now, this was extremely expensive, and a lot of index insurance products around the world and a lot of interventions as well, in developed areas, do not have the type of rigor as we did in this, because it is very expensive to collect that data.
If we can find a way to collect the type of data that most programs need to show impact-- this is data on consumption, data on income, data on health and nutritional outcomes, then perhaps you will have more programs undertaking the research so that governments, develop agencies, are designing more evidence products or programs for the rural areas.
Now, this is where our team went into unfamiliar territory for researchers. But we had to do this because we had designed a product, we had shown its impact, now what? These areas we're talking about, again, as Professor Christy had mentioned, are not low-hanging fruit. There are no insurance companies there.
And in fact, index insurance was a new concept for insurance companies. So who was going to build the capacity of insurance companies? Who was going to help them do the marketing for this population of pastoralists that they are not used to interacting with? We had to. So we had to develop capacities and skills to develop a whole training and marketing program, all across from the insurers themselves, to government agents, insurance agents, and to marketers.
This was a very costly proposition. When we started, this was OK, because we were doing this at pilot level. But now, when you're thinking of scaling up across the country, and you're thinking of servicing clients or interests in Senegal and Mali, you can't take your team and start training and so on. So you have to think of standardization.
Mobile technology helps quite a bit here, and I'll show you some of the examples of what we did. Low-cost, efficient delivery. This again, is nontraditional territory for insurance companies-- I mean, for research groups. But we had to get in the business of thinking, if we want this product to scale, how are we going to make it cheaper for insurance companies to deliver the service of sales? How are we going to make it cheaper for insurance companies to deliver information about the state of the index to increase trust? And we used mobile phones to do that.
Policy and institutional structure, well, we haven't yet started thinking about how we could use mobile phones for policy and institutional structure. But this is something we worked on in insurance. And as you have heard a bit from what also Florence has mentioned, there's a lot of policy questions that as we go more into mobile and start to unleash the power, the range of mobile applications for the specifics of scaling up the index insurance program, we're coming across, already, a lot of regulatory challenges that we need to start thinking about.
So that was IBLI, the IBLI program. How did we use mobile technology? In all those challenges I mentioned, as we try to figure out how we would support our stakeholders, insurance companies, or the government to handle issues of data collection, to handle issues of delivering service, to handle issues of training, we kept coming upon mobile as a solution.
But this-- the interesting part is this is the kind of territory we are talking about. There are huge numbers of mobile growth that you've seen in [INAUDIBLE] presentation, or you have doubtless heard about. These areas are areas that come on board much later. A lot more difficult. phone usage is lower. Mobile network coverage, only now is it becoming complete, in Northern Kenya, at least. Southern Kenya, we still have challenges. And yet we still found that mobile was quite beneficial. So imagine what it would therefore be in areas which are not as challenging as this.
So I want to highlight three key areas in which we leverage mobile technologies or mobile applications to help us begin to give IBLI a better chance to scale up. There's mobile phones as a service delivery tool. Mobile phones as a training and performance assessment tool. And mobile phones as a data provisioning tool.
Here, what you see is even in this area, this is the household formulas. This is our household data that we began collecting. The first round was in 2009. So these are the first six rounds. And I don't know if you can see clearly, and I'm not even seeing this.
But you can see that the light blue line is the number of households that use a phone every day. And that has gone from about 200 to over 620. And this is a sample-- I'd mentioned the sample was 928, but the balance sample across all the six rounds is about 720. So really, even in this population, you're seeing households not only having phones, but using a phone every day.
I have a sense that M-Pesa is a big part of this. But this shows the readiness of this population to take advantage of mobile phones. So let's get into mobile phones as a service delivery tool.
When we first started, I mentioned in 2011-- well, first of all, sales. So when we first started, the insurance companies had to purchase what they call point of sale devices. These point of sale devices into 2010 cost $12,500. So imagine, an insurance company, a pilot program, in an area that is sparsely populated, how many agents are they going to have?
When we started off, they had about 10 agents, and they were very reluctant to increase the agency. But you're not going to increase your sales and have impact if you do not have more agents. So we had to figure out how to very quickly give the insurance company a reason to increase the agency.
And we did that by designing offline mobile phone applications. This is how they look. And offline, at that time, because this was an area that was not fully network covered. So we had to develop a system that they could enter the data, and the data would stay in the phone until the phone got to an area that had mobile coverage, and automatically it would upload.
That helped us with another problem, too. The problem [INAUDIBLE] talked about, when they were first just writing receipts, and then the agents have to go and type in their receipts, the data was very dirty. And donors were supporting us. This was a supported insurance company. And such kind of data was unacceptable to them.
So we had to work with an insurance company to figure out how we could get good data. And that allowed us, of course, also to track who are repeat salers and so on. And right now, there are over 500 insurance agents, three different companies, and all of them using sales transaction platforms. We developed the initial ones for all of them, but they have now moved to their own platforms. Some of them have actually extended this type of sale transactional platforms onto their other business.
Mobile delivery of indemnities. Well, the first-- initially in Kenya, and even now in Southern Ethiopia, when the contracts were triggered, when there was a drought, and you had to make payments, we first would go with Land Cruisers, with the insurance companies, and drive around to the different areas where the pastoralists were. And as Professor Christy mentioned, some of these pastoralists are mobile, particularly during times of drought, when the contract was struck.
So finding the client and paying them cost a lot more than actually what the clients paid for. So of course, this is a pilot. A proof of concept. But it will always remain proof of concept if that is the case.
In Kenya now, they're able to move to M-Pesa. Not all these clients-- not all pastoralists yet have M-Pesa. Most of them do, but not all of them do. So in terms of financial inclusion, the insurance companies have made a policy that they will not issue any contracts to anyone who doesn't have a contract. Who doesn't have M-Pesa.
So you can say that maybe that is a problem of exclusion, and the government is now talking about it. But then the insurance company says, well, M-Pesa also excludes people that don't have M-Pesa. So how do you deal with that?
In southern Ethiopia, because of the costs, we had to work-- at least in southern Ethiopia, pastoralists are organized cooperatives, so the insurance agents will only work with the cooperatives, and it's the cooperatives' responsibility to then redistribute. And that is not redistribute, but to engage their members. The members, they'll get individual contracts. That is still, of course, a costly proposition.
Again, as a service delivery tool, agents would claim that they have a very big challenge of-- this is a difficult concept. And they would always say, look, pastoralists come, we try to explain this product, and then they say, OK. Well if this product is about drought risk, how did it work, or what was the state of the index in 2009 in this area? Because I remember that was a bad year. Or how was it after the long rain season in 2013? And there is no way to answer.
We developed an application that allowed them to answer this question. So this, we're calling it the IBLI Index Calculator. You can find it on the playstore. And basically, it allowed-- in all of the insurance units, you could go and type insurance unit, or county insurance unit, and the season, the year, and it tells you, this is on the NDVI readings. There are places in the app that will give you an explanation of what it is.
But basically, it then tells you, this is what the state of the index was. It was in the 43rs percentile, and there was no payout. You could even have a hypothetical contract, and say a contract of 10 cattle, what would I have gotten paid up. So this, the agents have all indicated that this is something very useful, and that all insurance companies have taken it up.
Now, to mobile phones as a training and performance assessment tool. I mentioned about 500 insurance agents at the moment. There is also government extension agents. If you think about the standard approaches to training rural development, you usually bring these agents together, or whoever you are trying to engage, and you have a trainer's manual. You maybe spend a day or two or three with them, and then you leave.
And first of all, that's very costly. Adult pedagogy will tell you that's also not a very good way to teach people. They will forget. But there's not much option. There hasn't been much option. You can try your best and make your training more interactive and so on.
But mobile phones can offer an option. Because with mobile phones, you can have refresher training. Because people can train today, and they can train tomorrow. Or in a week's time, you can send another set of questions and so on. So we started to develop mobile learning applications.
Now, this is called the IBLT Pocket. That was the name of the initial mobile curriculum that we developed. This IBLT is index-based livestock Takaful. Takaful is a Sharia-compliant insurer. And so we worked with them. We developed this basic curricula, and we tested to see, well, how well does mobile learning work for the agents?
And in particular, the question we were looking at, other than learning, does this learning have an impact? Now, we tested different types of learning processes. So you can have your mobile learning application, but who is to say they're going to use the application? You're going to want to incentivize it.
There are different ways. You can give them cash. That's what we did. But you could also, in the mobile learning field is what is called gamification. So using game mechanics and tools, things like leaderboards and so on to try and incentivize people. And the results were stunning.
This is a [INAUDIBLE] of one of our collaborators, Liz Lyons, an assistant professor at the University of San Diego. And if you can see this, well, you can't quite see [INAUDIBLE]. But that graph basically shows the four different experimental groupings; control, basic training, basic training with a cash incentive, and the basic training with gamification.
And those with cash incentives and gamification incentives sold over four times-- up to four times the amount of those that didn't. When we presented this to the CEO of Takaful Insurance Company, he immediately indicated that he wanted to ensure that all of his agents had to be certified after going through this process.
Now, there's another problem with that, because all his agents were not digitally capable. So right now we are working together with him to scan the environment of agents in that area to see if it makes sense for him to impose a requirement of digital agency for his-- digital capability for his agents.
That's a first step. But I think-- I mean, this has opened a really interesting research agenda for us. And the applications for this are way beyond insurance. I mean, the cost to extension, any type of development, intervention, or agricultural intervention, agricultural extension, is extremely high. And so the more you can move to mobile, the better.
But there are still a lot of questions. And we have very basic tool, and so now we're moving to try and test out more comprehensive content. Content that is delivered differently. If you push content daily or in smaller packets or larger packets, even now more, they're calling screen real estate. That it doesn't matter how big or small your screen is.
So there's a big agenda around the different types of gamification incentives. We've also started doing-- there's gamification, and there's game-based learning. And game-based learning is different in that it's actually a game. We developed game elements. So for our [INAUDIBLE] product, we have developed a mobile game application and are testing to see how that works.
And of course, in these areas, you have to think about language, you have to think about other context specificities. But this is something that we see as quite important and quite-- a rich agenda that can really help unlock one of the critical obstacles to a lot of agricultural development or adoption of important interventions.
The next is mobile phones as a data provisioning tool. I had mentioned earlier that the challenge of validating the satellite data, and that some areas, some green is not as palatable as other green. And so we thought, well, we're hearing people talk about crowdsourcing, and we said, well, let's try and see if we can use crowdsourcing.
And we got some of our pastoralists in one of the counties, Isiolo, and we basically developed an application to have them take pictures. And it was a simple application, because many of these were illiterate. But with a simple application-- this basically shows the different screens.
So the first one, you take a picture of the area. And then you get a set of screens that allow you to give a bit more information about that picture. Do you see trees, or you see bushes, or do you see grass? And how much trees, how much grass? Is it more green, or is it more brown? And what is the-- is it a sunny day, or is it a cloudy day, because that will affect-- and what is the carrying capacity, and what type of livestock; camel, goat, sheep-- I don't know if you can see those-- is most amenable or would prefer this type of forage.
And what we had is that in five months-- actually, this is the work we did with the Cornell Institute of Computational Science. And in five months, 112 pastoralists submitted over 120,000 surveys on vegetation condition. And here is a picture. This is not of all the 120,000. But it's a picture of where they were distributed.
So you see how they earned about 1.2 dollars a day for an average of 6.2 submissions. And there were experiments we did about this. Because you don't want to get in pictures these areas of high density and the areas you might want information. So you need to be able to push pastoralists or push people in certain areas.
And so we started to segment the maps and give more cash for observations in different places. And we saw that pastoralists were responding to that. So this was exciting, and there's a lot of work we're doing around that. Developing and finalizing the rangeland model to see if we can develop a filter for the remote sensing data.
The next level is image classification. We're not there yet. But you can imagine what image classification will do. Basically, after a while, you train the algorithm, and you can take a picture of the area, and maybe you won't even need an index anymore, or you won't need a-- because if you get it good enough, and it's GPS pointing and so on, that might be, one day, good enough.
But what this revealed to us is that there's so much more. If you can get pastoralists, who are not that educated, with very basic forms, to deliver this kind of information to you, there are a lot of questions you can answer. But imagine what else you can collect. We were talking about a lot of development interventions, agriculture interventions. The questions that they're asking are questions about how will this intervention affect consumption. Affect the type of nutrition, or affecting health.
Collecting health data, collecting [INAUDIBLE] is very costly. But if you could take a picture. And I don't know, maybe somebody sits in a certain way, flexes. You put a coin or a penny, and that penny is standardized, right? So you can imagine that well-- and these are things that we are now starting to work on.
But they're not that unrealistic. And I was at-- Ronnie, I saw you with your iPhone. With your iWatch. Is it iWatch? Or I don't know. I have a Fitbit. But I imagine a lot of people are carrying these watches. And if you're carrying these watches that are giving you information about your movements and so on, you also have an application there somewhere where if you want to, you can say how much water you're drinking, or the type of food that you're eating and so on.
And it's not that far fetched. And particularly when it becomes important-- well, not important. It's not that it's not important for us. But when you were talking about a nutritional intervention, and you want to know whether it really works, taking a picture of a plate of rice and beans can be much easier than the very expensive consumption diaries that there is.
And not only that-- I see my time is almost ending. But I'll tell you about a new, exciting agenda that this has led us to. One big problem is I mentioned the lack of information. And these are-- OK, imagine a pastoralist. They sell their livestock for a much lower fraction than they would otherwise sell it if they knew what the price was at the local area.
And livestock market information, the importance of it, there's evidence of that everywhere. Those who have access, or where there are such systems, the price of the livestock, on average, is higher. There's less variation. And it changes a lot of behavior in terms of market participation and so on.
And you look at some of these governments that we're interacting with, the Kenyan government and its Vision 2030 around agriculture. Ethiopian government has all the GTP too, the Growth Transformation Plan. You see there, invariably, the importance of market information systems.
And over the years, millions of dollars have been spent to try and collect such systems, but you can't even find the data. But there should be an easy way to be able to do that, and we think we could do it through crowdsourcing information. And crowdsourcing can allow you to do a lot more.
I'm sure many of you who have worked with rural surveys are also aware about the typical worry of whether your numerator is actually at the household, or if they're sitting by the tree. You can GPS enable, you'll find that out. You now have an indication of wherever he or she is getting that point, and of data quality and verification.
You can use big data algorithms. There's so much we can do around that. And so we are embarking on the process of it For livestock market information system in this area. And we were going to start with livestock markets because this is what we know. And because livestock, we like to deal with hard things first, because once we crack that, then I think we'll be able to scale.
But we have already developed a-- well, this is just a schematic of how our platform will look. I'm looking at the time. I'm coming to the end, so I think I'll just describe this schematic, because I think it's quite exciting. What you see is around here it says client organization, you reports or requests for specific data.
In our feasibility study for this, we have seen the government of Kenya, the ActionAid, WFBs, all collecting very similar information. They don't ask the same question. Well, I mean, they don't have exactly the same question, but in general, it is that they want the same thing.
So why should they all be paying for it? Why can't they just request a system-- we call it a system now. We have to think about what this system will look like; public private, will it be a company. You request the system. And the system has a sense of standardization parameters, so it submits to you the questions that it thinks you should be asking or that you want to know.
You accept that. It then transmits that to a range of volunteers, or just citizens. It will be transmitted to an area. Anyone with a phone in that area and that application can get access to their request for that data. Whether they will actually be allowed to provide the data, that will depend on their skill set. And these are things that mobile training or remote training can do.
So they collect the data. They get the sets of incentives. They push this back in the database. And the database can then collate all of that information and spit it out in the format that the client wants. If it's a farmer, maybe it's SMS. If it's the government, maybe it's a dashboard. If it's a radio station, maybe it's a report on the types of prices, so they could submit it to their listeners.
So we have developed this prototype. This is a phone-based task system asking-- we'll be asking [INAUDIBLE] questions. You can take some photos. You'll receive certain amounts of payments.
Now, this is the front end. There has to be a back end. Every time we talk about mobile, there's a lot working in the back. So in the back, you are-- like a questionnaire. You have to design your questionnaire. And then as a questionnaire, you'll have to figure out just how many data points you want. And there's a lot of work around that that we're doing.
And this is eventually how-- this is a schematic of some of the outputs. So we have a prototype that's in test. A pilot that's going to go out over the next two to three months. And hopefully we should have initial insights by the third quarter of the year to at least be able to take to donors, investors and think of how we might scale it up across different counties and to now broaden beyond livestock markets.
So this is my final slide. I don't know if we'll get there eventually. But this is-- so basically what I was trying to do is not mobile money. But the idea is that I'm hopeful that there's really a lot-- I mean, the power of mobile can help to unlock a lot of the challenges that are faced, not just with insurance, but with a lot of interventions in the rural agricultural space.
You can think of mobile phones as an asset, providing access to information, providing access to opportunity. When you talk about-- I didn't mention the labor disrupting properties of this, but this enumerator, who is sitting under the tree, collecting data, he will be disrupted out of the system. Because now it will be the lady herder, as she's taking her goat off to market, she gets-- oh, I think they want information on the price of milk. And now she'll be able to contribute information and at the same time receive incomes for that.
So it's about an unlocking of underutilized resources. And things like Uber, Airbnb, they're all about the same thing. And so actually, as the team and I were brainstorming about this, we were using-- OK, this Uber and Airbnb, how can we bring this to our environment.
Beyond access to application. You might be hearing a lot about access. But access, already in areas like the drylands, if people are getting access to increasing mobile density, they have access to mobile phones.
The next thing to be thinking about is applications. Because you've heard about the safari coms. Now we're hearing Google and Facebook are all-- Amazon-- Google was already in Kenya. They started a pilot.
Have you heard of the balloon? The Google balloons. They're trying to put across a system of balloons that will deliver Wi-Fi across the world, and Facebook is battling with them, and so on. So access is no longer going to be a problem.
The question is what do you do with that access? What do you do with the application? So I say beyond money, money is still important. You've got to pay those crowd sources. You've got to-- money is that transfer of value.
So yeah, let's think beyond money. There are other applications. But I think I just put that money scheme there because I'm supposed to be talking about money. But I believe that.
Knocking on the door of big data and its engines. I mean, this is what I was describing earlier. I mean, once you get all of this data-- one question I am going to ask [INAUDIBLE] about is that data set he was talking about is amazing. I don't-- grad students around here, you should be talking to him about access to that. Because there's a lot of insights that can be unlocked from that kind of data.
And then of course there's the regulatory issues. The issues of security, ownership. When we are dealing with these things as a pilot, you might not really consider the importance of this. But as a team, even as we were doing the prototype, we had to step back and say, well, we might be doing this on a small scale, but this is still people's data. We need to start considering from this point on how to deal with the issues that come about. So I hope I've made a case for mobile applications in rural agricultural space.
SPEAKER 2: This has been a production of Cornell University. On the web at Cornell.edu.
Dr. Andrew Mude is a Principal Economist at the International Livestock Research Institute (ILRI) based in Nairobi, Kenya, and a graduate of Cornell University. He was the keynote speaker for the Symposium on Mobile Money, Financial Inclusion, and Development in Africa on April 21, 2017.
His work deals largely with developing innovative, evidence-based technological solutions to ensure the productive and sustainable use of livestock by dryland populations. Dr. Mude leads the multi-award winning effort to design, evaluate and scale livestock insurance to help millions of poor herders and their families who care for and depend upon their livestock under considerable drought risk.
Widely published in peer-reviewed journals and featured in numerous prestigious media outlets across the globe, Andrew was the 2016 recipient of the Normal Borlaug Award for Field Research and Application recognizing exceptional, science-based achievement in international agriculture and food production by an individual under the age of forty.
He earned his PhD in Economics from Cornell University in 2006 and was a Mid-Career Fellow of the Sustainability Science Program at Harvard’s Kennedy School in 2011.
