Today is somehow a perfect way to solidify our ongoing rich cooperation and somehow a perfect start of the year. This is the first signing ceremony of the MSDCF that we have this year with you and Saint Lucia in the Caribbean. So let me therefore again take this opportunity on behalf of the United Nations team to extend our best wishes for the new year to yourself, Prime Minister, and then to the Government of Saint Lucia and the people of Saint Lucia. It's a significant day for all of us as we formalize as you know the signing of the UN Multicountry Sustainable Development Cooperation Framework which will cover the years 2022 to 2026 and we do this after a year of consultations at various levels. So we have arrived at a point where we believe we have a clear roadmap on how the UN will support the various Caribbean countries and territories 22 in total including Saint Lucia and basically to achieve two urgent priorities. One, to accelerate progress towards the attainment of the sustainable development goals and in that context you know that this is extremely important more urgent than ever and two, to facilitate a faster recovery from the socio-economic and health impact of the COVID-19 pandemic. And we tend to refer to this cooperation framework as one that has never been as significant as this one because we are confronted with a new phase every day extraordinary circumstances. This global pandemic alongside the climate crisis which poses an existential threat to the region deteriorating fiscal space with increasing unsustainable depth levels and somehow we think that we are at risk of losing years of development gains that have been so hard to gain and also a risk of decelerating progress towards the sustainable development goals. So Prime Minister we know that you're a very strong advocate for putting people first and I think you would appreciate this people-centric five-year cooperation framework. Allow me to just list the four key priorities which are critical we think for achieving resilience and sustainable development in the Caribbean. First and unlike actually the previous cooperation framework we have increased our support to the economic resilience agenda for example to improve business competitiveness and enhance government efforts to diversify their economies as well as to manage a sustainable level of debts. And we came to that conclusion together that these are absolute imperatives for economic recovery from COVID-19 but also for mitigating against future economic shocks. So under this area Prime Minister we also look forward to supporting the youth economy agenda of your government and we have discussed and as you know we're going to launch in the coming weeks a new joint program with several UN agencies under the SDG fund on innovative finance and St Lucia is one of the two countries that will directly benefit from this including specifically for this youth agenda as well as debt management. Secondly we'll intensify our efforts to ensure that no one is truly left behind. By furthering the human rights agenda we support policies to eliminate all forms of discrimination and measures as well to reduce structural inequalities especially for the most vulnerable and that's probably extremely relevant in that context. So that will include the force to ensure equal access to quality social services with ongoing work on health and educations as well as shock responsive social protection. And I must say Prime Minister that our joint program for universal adaptive social protection in partnership with your government has already been reaping significant benefits as it relates to the provision of cash transfer and top ups to the most vulnerable in St Lucia. So we really hope to leverage on this success which is really being promoted as a best practice in the region to further support those most at risk in St Lucia and I know that agencies continue to expand this work in your country in this area. Thirdly in the area of climate change the MSDCF reflects very strongly our commitment to enhancing national capacity for gender responsive disaster reduction but also support the climate change adaptation agenda and at the same time we strengthen our support on natural resource or ecosystem management. The fourth pillar finally we deliver the UN support for ensuring that Caribbean countries remain peaceful and just societies. So the UN plans to further scale up its corporation for fighting gender-based violence, increased access to justice, promote social cohesion and also improve community safety and security. And again we know this is an area of your interest for by your government so therefore we'll continue to further our cooperation especially in this area of security and crime prevention. Finally to achieve this priority new partnerships will need to be to be forced to be scaled up while intensifying citizen engagement so we'll explore with you and your government opportunities for more innovative and sustainable financing for example but and I will end with this as an immediate step we'll operationalize our commitment through the ongoing preparation on the first two-year country implementation plan specific to St Lucia. So basically this two-year country implementation plan for 2022-2023 which is being finalized at the moment will translate the broad commitments of the corporation framework into a very tailored plan of actions that will bring together the entire UN system as one behind this ambition and this is a fundamental change in our business model that we had already piloted last year. So we look forward to your leadership Prime Minister to finalize this plan of course so that it reflects well your expectation and is aligned with the national priority in St Lucia. So again I want to thank you for your trust for the partnership with your government you can count on us you see you have a team that is widely represented today to precisely deliver on these ambitious commitments and to support you in this difficult moment so again best wishes to to you and to your government for 2022. Let me first of all thank you for having St Lucia involved in the signing of the multi-country sustainable development corporation framework. It's happening in these times when all the news always appears to be bad that there is a blame of hope from our friends in in the UN. As you know our country has been ravaged by the swift wave of the COVID pandemic and all vulnerabilities of our of our country they are exposed our health systems our health system is fragile it's under pressure our education system is even on the on on the tremendous pressure as we have to debate whether we can have children at school or children at home so it's a very difficult time so it's happening to to be here this morning to sign this framework and I'm very pleased with the priority areas that have been set by the UN and and said by DDN this morning our size as a developing nation and the complexities of our social economic situation demands an approach to development that involves people and it is the solemn mantra and belief and objective of my government to put people first. So the UN has a long history of working with governments in the region to address these critical issues. The signing of the multi-country sustainable development framework provides an opportunity to strengthen our partnership with the United Nations and to reform our commitment to the development activities towards the 20-foot agenda. The 20-foot agenda for sustainable development outlines ambitious goals to achieve a better world for all by 2040. It creates the avenue for collective action and efforts and joint effort by government civil society and every citizen to build a better future. The COVID pandemic has demonstrated the need for a unified approach to transform our social and economic realities as a country. Over the past two years the pandemic exposed the fragile nature of our country. We witness rapid increases in unemployment as the key economic sector of tourism almost grinding to our hearts. As a government with a people focus agenda we are aware of the resulting effect of the pandemic on poverty and the quality of life of our people. We welcome this opportunity to collaborate through the UN to advance the agenda for 2040 which represents a plan of action for people, planet and prosperity. The same thing sustainable development goals that support this agenda compel us as a nation to fund innovative strategies to end poverty, address inequalities and address the pressing issue of climate change. As we continue to make progress towards these SDGs we acknowledge our achievements but remain aware that we have a long way to go. Despite the challenges in fulfilling the obligations of the SDGs the government solution remains resolute to put people first. Against this background we embrace the philosophy of the UN that is to leave no one behind. The multi-country sustainable development framework is consistent with our development strategy to strengthen our social and economic resilience as a country. To achieve this we will pursue policies that influence the systems to improve access to quality healthcare, education and a conducive safe environment for every citizen to prosper. In essence we will make every effort as a government to reduce the risk of being left behind. The multi-country sustainable development framework provides a framework to address challenges and vulnerabilities including but not limited to our financial and economic vulnerabilities so please address this morning our inadequate social systems and weak disaster risk management and sustainable management of our natural resources. The framework defines eight outcomes that the United Nations together and the regional partners have resolved to jointly focus efforts for the next five years to shift the socio-economic trajectory of the Caribbean. The Government of St. Lucia will work with the UN to address these bottlenecks that hinder the organization of benefits associated with these outcomes. The endorsement of the multi-country sustainable development cooperation framework then restricts the willingness of the Government of St. Lucia to contribute to significant structural changes in the economies and governance systems. To those equity for all, resilience to climate change and shocks and peace, safety, justice and the rule of law not forgetting economic resilience and shared prosperity for all. The implementation of strategic actions to realize these broad goals require resources. The identification of resources to address the demands associated with our socio-economic realities remain a pressing challenge for us as a developing country. Our limited resources were stressed to respond to health and economic problems associated with the pandemic. Our policy responses resulted in increases in public debt beyond established potential ratios. What this means is that our economic stability is at risk. We are more vulnerable. This signals the need for responsive policies and strategies and help from our friends. I am indeed pleased that this framework will serve as a platform for the UN to provide more effective support not just to St. Lucia but to the region as a whole. We shared a vision of the UN for St. Lucia and I quote, the region to become more resilient, possess greater capacity to achieve all the SDGs, and become a place where people choose to live and can reach their full potential in peace. At this juncture, I take this opportunity to thank the UN for this invaluable intervention and to reiterate the Government of St. Lucia's commitment to building the socio-economic resilience of our country. This framework is an important instrument that will contribute to the efforts to build back better and to hasten the recovery post-COVID. We look forward to the continuous support of the UN as we work together to our national development goals and continue to put beautiful. I thank you.


For more videos on people's struggles, please subscribe to our YouTube channel. There is no end in sight to the COVID-19 pandemic as the number of cases and the number of casualties continue to rise. Along with this, the fear among people is also increasing and economies worldwide are falling as people are staying home in government-imposed lockdowns and self-isolation. While self-isolation is an important and necessary measure for containing the spread of this highly contagious virus, it is not a measure everyone can afford to take, especially in these precarious times. It is also not a sufficient measure by itself. Left parties and organizations across the globe are calling for governments to take steps that ensure the protection of people, particularly the most vulnerable sections, and also ensure that people are prioritized over profits, wherein power left governments have enacted extensive measures to ensure that people and their livelihoods are protected. The most striking example is of China's, where as of March 20, no new domestic cases had emerged since three days. One of the most important measures called for is scaling up of testing so that free tests can be availed by all those who need it. In countries where healthcare systems are largely private and insurance-based, left groups have called for free testing of everyone, regardless of whether they have an insurance card or can pay for it or not. To do this, governments will need to allocate resources to the public health sector immediately and ramp up production of testing kits. The capacity of public hospitals also needs to be increased to accommodate the rising number of COVID-19 patients and provide them with the intensive care that they need. Many left organizations are citing the example of Spain, which nationalizes its private hospitals in order to deal with the crisis. They are calling for a similar step of a partnership with private hospitals to provide free treatment to everyone in need. Similarly, organizations such as NUMSA, the National Union of Metalworkers of South Africa, have also called for abolishing of private healthcare. There is also a need for setting up temporary quarantine centers to house people showing mild symptoms so they do not infect more people. There are also calls for strengthening the pharmaceutical industry to increase manufacturing of sanitizers, medicines, and other necessities. Apart from the general population, the medical staff needs to be armed with all the necessary protective gear, as they are in the front lines fighting this battle and they are the most likely to get infected. Moving on to the matter of social distancing. Governments across the world have recommended social distancing and self-isolation as one of the first measures of defence. But what is also being recognized is that social distancing is a privilege and not everyone can sustain themselves in quarantine. Workers who work for a daily wage or hourly wage, workers in the informal sector, and gig economy workers mostly do not have the kind of benefits that will allow them to stay at home. They mostly do not have sick leaves or paid leaves. They also have very little savings to be able to afford time off. Gig economy workers like Uber or Lyft drivers and delivery persons are also at greater risk as they come in regular contact with the general public as part of their work. And as this health crisis spreads, their earnings will be reduced further. In these conditions, these workers will be forced to continue working even in sickness and if infected, they will only spread the infection with them. These workers are among the ones at highest risk right now and need to be protected. Left organizations everywhere have called for protection of incomes of workers. This can be informed of direct cash transfers on ensuring sick paid leaves and unemployment benefits for all. Trade unions have also called for a moratorium on bank loans to small enterprises and credit lines for affected people. In Italy, workers in factories went on strike, demanding that employers take measures to make the work site safer. They demanded that the work site should be reorganized to ensure social distancing and that workers should be provided with protective gear such as sanitizers. Across the world, social movements, organizations and trade unions have set up hotlines and made calls to workers to denounce violations to their labor rights, such as forcing them to go to work, retaining pay, arbitrary firing, etc. that many workers, especially in the informal sector, are facing right now. Another essential requirement for self-isolation is a safe space or a home to stay in. To ensure this, left groups have called for freezing any rent, increases freezing evictions and banning all four closures. There is also a call for giving homes to the homeless. In the US, for instance, there are 17 million vacant homes according to the Party for Socialism and Liberation. This is more than enough to accommodate the 500,000 homeless people. And of course, if people are to remain in their homes, governments also need to guarantee them a steady supply of food and other health needs. Left parties have called on governments to increase the ration limits for poor families and use national food reserves to meet these needs. In many countries where students get food at schools, organizations have called for this food to be delivered at home. Children will be even more dependent on this food right now as their parents' livelihoods are compromised. The Indian state of Kerala, which is governed by a coalition led by the Communist Party of India Marxist, has also delayed deadlines of water and electricity bill payments. They are also working on laying down new lease lines to increase the bandwidth of internet as consumption is predicted to increase with more people being at home. Other organizations too have called for ensuring that no utilities are cut at this time. It is also important to enact measures for populations living in vulnerable environments. This includes the homeless living in crowded shelter homes. This also includes refugees, migrants and other displaced persons living in overcrowded camps. These can become centers of outbreaks if adequate care is not taken to protect the inhabitants. Incarcerated populations are also at risk. For instance, the Palestinian Prisoner Society reported that four Palestinian prisoners had contracted the virus in an Israeli prison. But they are not receiving the required care or treatment. The only measure taken by the Israeli authorities is a ban on visits by family members and lawyers, which is only worsening the ordeal of the prisoners. Left groups have called for the release of elderly prisoners and petty criminals. They also have called for the release of persons being held by ICE, that is the Immigration and Customs Enforcement, and a freeze on any new arrest. On March 18, prisoners at Lapikota Prison in Bogota, Colombia mobilized inside the Penitentiary Center to denounce that the prison authority has not taken any measures to improve sanitary conditions and that the severe overcrowding lack of water and other proper conditions poses a severe threat to the lives of the incarcerated population. Social organizations in Colombia have called for strict sanitary measures to be adopted by prison authorities as well as the humanitarian release of prisoners accused of nonviolent crimes, a measure that has been adopted in several countries in light of the pandemic. There is also a call for ending sanctions on Iran, Venezuela and other nations. These sanctions have made it difficult for these countries to procure the required medicines and health equipment worsening the pandemic. The COVID-19 pandemic has laid bare the many flaws in capitalism, which is in no way equipped to deal with the crisis of this scale. The end of this crisis will only increase the already existing inequalities in society. Socialist countries and governments are right now able to control the spread of the virus and at the same time ensure that people's needs are met. Not only are they dealing with the crisis in their countries but the socialist governments of China and Cuba are also extending solidarity to others by sending in medical teams and equipment. If we are to find our way out of this global health crisis, their example will need to be followed.


Hi everyone! In this video, we're going to talk about mutable and immutable variables in Rust language. But first of all, make sure to subscribe to my channel. More subscribers means more quality videos on my channel. So in the comments section, you can put a comment or link on anything related with Rust language or programming in general. Okay, let's back to the topic. Suppose that we have a variable and someone wants to change this variable somehow, somewhere and maybe that can produce a bug in our code. We don't want to make some of our variables to be so easy to change their values. So to prevent this in Rust, we can use immutable variables. That means that we can't change its value during code execution. All variables in Rust are by default immutable. So let's make a variable. This variable is going to be immutable by default. So let's make a variable my variable and let's say that it has value 5. Okay, let's print our variable. We will get number 5 as printed result. But what will happen if we want to change this immutable variable? For example, let's try to say that my variable now is 6 and let's try to run it. We're gonna get an error which said that we cannot make new assignment of our immutable variable my variable. Our variable is immutable. We can't change its value. But if we want to change value of our variable, first we need to make it mutable. And we're going to do that by using mut keyword and now we can change our variable. Okay, let's run this code again and now we can change this variable, but we're gonna get a warning. We're gonna get warnings all the time in Rust language. That's good because we will not have big number of bugs in our code. So this warning is tell us that value of my variable, which is 5, is never read. So we can use this value of 5, for example here by calling this my variable, which has value of 5 and add, for example, number 3 to it and let's run this code again. And now we will get result number 8. So all variables in Rust language are immutable. That means that you can change their values during code execution. But if you want to change their values, you need to use keyword mute to define that your variable can be changed. That's all for now. Don't forget to subscribe to my channel. See you in the next video. Bye.


So it's really my pleasure, well, he's not late after all. So introduce Dr. Manisha Shah. She's assistant professor in the Department of Economics here at UC Irvine. She received a PhD at UC Berkeley and has been interested in the intersection of development, economics, applied microeconomics and health policy. Recently she's investigated the influence of economic incentives on risk behavior, choices and health outcomes of poor women in developing countries. She also conducts research in the area of child health and has written papers on how child development affects maternal labor supply decisions as well as the allocation of resources within a household. She's collected data from field work in Mexico, Ecuador, Brazil, Indonesia and India. So if we are nice, maybe she'll take us with her on one of her frequent trips. Today she's going to talk to us about her research, randomized evaluation of the life scale sanitation program in Indonesia. Welcome. Thank you. Thank you, Deli. And thanks for coming today. So I should mention also that I'm happy to take questions throughout the talk if you guys have any questions that come up. So as Deli mentioned, I'm going to be talking about some work that I've been doing actually the past few years in Indonesia looking at some of the impacts of a randomized impact evaluation of a sanitation intervention. And I should also mention that this is joint work with Lisa Cameron who's a colleague at Monash University in Melbourne. Okay, so just to give you a little motivation here about sort of child health issues in the developing world. I have some statistics here. One child dies every 15 seconds from diarrheal diseases in poor countries. Both diarrhea and acute lower respiratory infections account for more than 40% of the 10 million annual deaths that occur among young children in poor countries. And while we know that there is good evidence that improved sanitation decreases diarrheal diseases, we know that but if we look at things like sanitation measures, we still see that a large proportion of the world's poor actually lack access to improved sanitation facilities. So again, I have this estimate here that 60% of the world's poor or a little less than 3 billion people actually lack access to improved sanitation. And in the country where we're working in Indonesia, in the last year about 19 million people also lacked access to proper sanitation. And so if we think about the disease burden, the greatest disease burden of these lack of sort of access to sanitation fall on kids. So if we look at infants and children under five years old, the greatest burden of disease and death occurs amongst this population of children. And so of course the idea here is that diarrhea, by reducing normal food consumption and nutrient absorption, diarrheal diseases then can cause, a significant cause of malnutrition, leading to potential impaired physical growth, impaired cognitive development growth. And then there's also reduced resistance to other types of infections and all sorts of other long-term health impacts. For example, long-term GI disorders. So those are sort of the health impacts. If we also, I'm an economist, so I also like to think about a lot of the economic impacts. Though today this is a public health talk, so I'll be talking to you mostly about the public health outcomes we've been looking at, but just sort of as a little background and motivation, there's also a lot of important economic impacts we need to think about. There's just all the costs associated with illness. So people who are sick, they need to spend money to go see doctors, medical treatment, et cetera. And then there's also all the costs associated with productivity losses. So when kids are sick, they miss school. Miss school means the potential for long-term lower wages. So when adults are sick, they miss work. So there's productivity losses in terms of missed days from work, missed wages, et cetera. And so while I said there is pretty good evidence that sanitation decreases diarrhea, a lot of the current evidence that is out there of the health impacts of sort of sanitation and hygiene interventions, they come from studies that impose very controlled conditions in fairly small populations over short time periods. And so part of this study, I guess, is to kind of look at a much larger program, look at a larger population, look longer term. So we're going to be following these households a little over two years. And then the other nice thing about the study I'm going to tell you about is we have a really nice design where we have this randomized design. And so we're actually going to be able to say something a bit better in terms of, you know, we're going to argue that we have nice causal estimates of looking at the impacts of improved sanitation on health outcomes. Okay? And for those of you, I guess, who don't know where Indonesia is, this is Indonesia, and we're going to be doing work in the province of East Java, which is just this red circle here. All right, so let me give you a little background about what the actual intervention looks like, and then I'll tell you about what we did. So the program is called Total Sanitation and Sanitation Marketing, and this is a program that's being implemented by WSP, which is the Water and Sanitation Program, which is a group that basically comes under the umbrella of the World Bank, and they have different offices throughout the developing world in different countries. And so these guys have been doing, you know, water and sanitation work for a long time. But this program, so it's called TSSM, and in Indonesia it's actually called STOPS. This program is sort of a second generation program of CLTS, if any of you guys have heard of CLTS, it was Community-led Total Sanitation. And so the idea behind the program or what WSP was doing was they, you know, their sort of long-term goal is they want to roll out this program, the sanitation program throughout the country, but they started with the population of rural East Java, which is actually, you know, the third most populous province in Indonesia. And so they were piloting this program at a large scale. There's 25 million people that live in rural areas of East Java. And the goal of this program is basically to get communities to become ODF. So ODF stands for open defecation-free. And so there's still a lot of open defecation that goes on in rural communities, whether it's in agricultural fields. In East Java it's primarily in rivers, because most of these villages are, you know, they're based on these rivers, so people do a lot of their open defecation in the rivers. And so just to show you some data here, this is at our, you know, at baseline, we have about 65% of households that report to still practicing open defecation. And these bars are just, you know, we're going to be looking at different districts in East Java, so this is just the variation across the different districts. But, you know, you'll see that the prevalence of open defecation is still quite high. The other important thing to know about this program, TSSM, is that there's no funding for capital works. And so the idea here is that it's not that WSP is coming in and giving households money to build toilets. They don't do that. I guess, you know, a little historical background for why they don't actually do that is in the 1980s the Indonesian government under Suharto actually went in and built a lot of community toilets in rural villages in Indonesia. And they found that people didn't use them. They sort of fell apart. And so this new round of sanitation programs in Indonesia, that the idea is they want these things to be demand driven.  But they want this to come from people themselves. You know, there's very much this feeling if we just go in and build people toilets, they won't use them, they won't care. They're actually spending their own money to build toilets. Then we'll actually see more of this sort of technology adoption happening. Okay? And so the, I guess what that leads us to is the intervention is pretty, you know, when we heard about the interventions, I was sort of thinking, is this even going to work? Because it's really very much just a lot of information. So the way the intervention works is some guy comes into your community and he spends a few days there. And the first day that he's there, he basically says, let's talk about your current sanitation practices.  So-and-So, where does your household defecate? Mrs. So-and-So, where does your household defecate? And then they sort of walk around the community and they point out where these households defecate.   And then, you know, that's kind of the first part of it. And then it's like, look, you guys are generating all these poo. Now let's look at your kids. They're running around barefoot. They're ingesting feces. They're getting sick.  What kind of change can you do? And then the idea is that this facilitator, you know, this session is kind of called triggering. And the idea is that he's going to trigger change. And then what he does then is sort of spend time thinking about if you guys do want to change, what can you do? You know, like how can you build toilets? How much might it cost? Where could you get the supplies? And so that's basically the intervention. But there's no, you know, we're going to give you money to build toilets. It's if you want to build a toilet, let me help you plan how you might build a toilet. But you actually need to come up with the money to do that. So two things. So first, do we have good data on the incidence of viral disease per rate of open defecation in the community? I mean, I see that. That's a great question. Let's imagine that we were perfect public health professionals that have all the information. Is that information available? So no. And I think that's a really good question because we also went into this wondering, you know, in a way these people right now are pooing in rivers. And there is a question about if you actually build a latrine, what that does mean is that the poo then sits nearer to where you live. And if you're not cleaning that area properly, there is actually a potential where you could have, you know, kids, for example, could be more exposed to feces in this area versus if you're just going out walking to the river, you poo and it kind of goes away, right? It really goes. Exactly. And I can say as a population, we have a higher incidence that doesn't directly observe the negative health consequences. So that's one of the second things. Just to kind of emphasize your prior, you really wouldn't expect, I mean, there's a lot of kind of health behavior theory that would say we don't expect much at all with this because we're not giving anybody income. Exactly. And presumably they had some information before you came in. Exactly. And so that's exactly what we're going to, exactly. So we sort of walked into this thinking, is this even going to work? And I guess I should also give you the background of what we actually wanted to do was have an additional arm where we incentivize households where not only, you know, we sort of gave them the usual treatment, which was this information walk of shame thing, but we also said, and here's 50 bucks to actually build a toilet. But WSP was very against that. And, you know, because this is sort of the program they run, we were coming in to evaluate the program they run, and they didn't want us changing it, which was, you know, fair enough, right? And I guess, which then brings me to this point, we still had to convince them to randomize, right? They don't randomly allocate their program. And when we came in saying we want to do this intervention, and I should, let me step back also and give you a little more background about why we came in. So Bill and Melinda Gates Foundation is interested in investing a lot of money in water and sanitation right now. But it's sort of, they're not exactly sure where they should be as investing the money to get their biggest bang for their buck. And so what they've done is they've funded a few evaluations of sanitation programs in a few developing countries. So this project, I'm the PI in Indonesia, and they're funding this work. They're bringing another program in India, another one in Vietnam, another one in Tanzania. And so the idea is that they're going to look at all these evaluations, learn something about what works and what doesn't work, and then they can kind of go on and, you know, invest in sanitation. And so because of that also, you know, part of this was okay, we're evaluating this program. These are all, every country is evaluating programs that currently exist and are, you know, currently in the field. And so we, you know, we met with a lot of resistance from WSP in terms of trying to sort of change what they're doing. And, you know, but the nice thing that they did agree to do for us is that they did agree that for their phase two rollout, the part of the program that we, you know, the phase of the program that we would be evaluating, they did let us randomly choose villages. And so the nice thing here is that for the evaluation that we're doing, we're actually able to compare, you know, randomly chosen treatment villages with randomly chosen control villages. And so the idea with random selection, you know, we're going to show you that because we have this random selection, the idea here is that the treatment in control villages and the absence of this intervention would otherwise be exactly the same. Okay? And our data pretty much does back that up. And so what's nice about that then is we don't have to do kind of one of these pre-post studies where then we might worry that other village level characteristics or other household level characteristics might be driving the differences in health outcomes that we observe. Okay? Let me ask you a question about this scale. It's 20 million people in this region. And I don't remember the population of Indonesia. So what proportion of the country is the life scale? This, so the Indonesian, it's about 10% of the total population. So yeah, Indonesia, the size of the population is fairly similar to the size of the US. Right, and so East Java, there's 29 rural districts in total. And as I mentioned, the WSP was rolling out the program to all the rural districts. We got in in phase two. So we're evaluating phase two of the program. There were 10 districts that they had selected for phase two. And in the end, we have eight districts that are participating in the study. And that's primarily just because of timing and our surveying and et cetera, et cetera. And so what we basically did was we went to each of the districts and we said, you know, give us a list of anywhere from 30 to 70 villages where you're planning on, you know, running this program. And then we, so we got their list. And you know, most of the district offices between 40 and 70. And then what we did was from those lists, we randomly selected 20 villages where 10 of those villages became our treatment villages and 10 of those villages became our control villages. And then we kind of sent that information back to them and said, okay, now you can go and trigger in these treatment villages, but please try everything you can, you know, that's possible not to trigger in the control villages. I'll show you some data on compliance in a minute. And then what we did was we, you know, we had our survey firm that went into each of these villages and they, they basically mapped the universe of households with kids under five. And then we randomly sampled 13 households from each village, from each of our treatment and each of our control villages, which gives us about 2,100 households in the end who will be following over a few years. Okay. And so I guess I don't know if you guys can see this. I don't think you really can. We have an RA who does nice GIS work for us and made this, who made this map. And then it's just to show you, so this is the province of East Java and these are our eight districts that we went to and these, you know, the kind of, the diarrhea colors villages are, our treatment villages and then the kind of, the purple are our control villages. But we have, you know, this is just to kind of show you that we're going all over the province and we have pretty nice geographic heterogeneity. So time frames. So we, you know, we, we've been working on this project for quite a while. We got participation and agreement from all the districts in, you know, May through July, 2008. Then we went in, after we gave them the list of the treatment and control villages, we went in, we ran our baseline surveys starting in August, 2008. Ending in September, 2008. And then right in October, those facilitator guys go into our treatment villages. They do their triggering. And then we kind of let these households sit for a while, right? Because we're interested to see if they're actually going to build a toilet or what's going to happen. And so you can't kind of go follow up really quickly because it might take households a while to build these toilets.   You know, did the program actually come into your village? Were you guys even treated? We also collect some diarrhea measures over time because there's, you know, diarrhea changes a lot depending on seasonal issues, et cetera. And, you know, and now we're just basically working on some of the final analysis. Sorry, I missed it. Were you able to give them the $50? No. We were not. We were not. But the findings from the study will be good motivation for why we should be able to go back and do that because I'm basically going to show you the main reason that people don't build toilets is because they don't have the money to do it. So the outcomes I'm going to show you today are primarily health related. And so the idea here is we collected various levels of health outcomes. So the biggest movement you would quickly see with kids under five is diarrhea, right? And so we have, you know, we have diarrhea. We've also collected fecal samples to get at this issue of do we see any changes in, you know, in parasites. And then if you, for example, observe these changes in diarrhea, well then you might think that you would also see changes in anemia, right? Because if kids are keeping more of their food in and there's more nutrient absorption going on and iron is increasing, then maybe you'd see sort of improvement in anemia. And again, similarly, you know, if there's less diarrhea happening, we collected information on high weight, arm circumference, head circumference. Again, with the idea that if, you know, these kids are getting healthier, you should see some movement here on stunting and wasting measures. And then this last, this is a much more longer term measure, right? So there's some evidence that, you know, you would see improvements in cognitive and motor development, again, if kids tend to be healthier. And so we had a psychologist working with us who developed this, there's this ages and stages questionnaire that's commonly used in rich countries. And so she sort of adapted it for the Indonesian version of it, okay? And so we, you know, we have, in terms of our baseline and end line survey, we have collected lots of information. We have all the general household level basic demography, labor market outcomes, income. And then of course, you know, things on water supply, sanitation facilities, sanitation behavior. And I'm going to show you lots of data from these surveys. For the kids under five, we collected all the health outcomes that I just mentioned in the previous slide. We have, as I mentioned, we also have this longitudinal data set where we went back every few months to see if the, you know, how kind of treatment and control communities. You know, you'd be worried, right, that we were picking up in this longitudinal survey that all of our control communities, for example, have been triggered or been treated. So we just wanted to keep an eye on what was going on there. And then we have the end line data set, which is basically the same as our baseline household survey, but we actually were able to also get some funding to collect fecal samples. So we didn't collect fecal samples in the baseline because it's pretty expensive to do that, but we were able to do that in the end line. And then we also have a community level survey, which again, I'll show you a few indicators from that. But here the idea was we just wanted to get a sense of, you know, at the community level, what kind of public goods are already being provided, what sanitation looks like, what water looks like, and what other existing programs are, you know, going on in the communities. Just to make, you know, because again, you'd be worried, say maybe there's other sanitation programs going on that are affecting these outcomes. And so we just want to be able to make sure that what we're measuring as our treatment effect is actually the effect of this program and not other things. So I'm not going to show you, you know, we have pages and pages of table in the paper showing that we do actually have very nice balance between our treatment and control households. You know, so one thing you would be worried about is if at baseline before the program happens that you have big differences in treatment and control households. But, you know, we don't actually, in fact, have very many significant differences. And so this is just to give you an idea, you know, before the program goes in, income of these households look the same. Access to sanitation looks the same. Diarrheal rates look the same. Anemia looks the same. All these other Z-scores that we're going to generate from our heart and weight measures look the same. And so it seems like we have pretty good balance across our treatment and control villages. I thought you picked the non-control, the therial color part of the map. Was it because they had higher incidence of therial? No, no, it was just, it was all... Exactly, we didn't... So the only thing we observable that we picked on was we needed to make sure that the households had children. And that was the only thing we stratified on. And then I think, you know, the other thing just attrition is a potential issue here, right? Is if we have... We clearly will have some attrition, but it matters again if you have differential attrition by treatment control status. So we spend some time looking at that in the data. And again, it doesn't seem to be the case. You know, so we have about 8.5% of our households we lose when we go into the follow-up. But we look at characteristics of these households that we lose and they don't look different across treatment control status. So it doesn't seem to be the case, you know, that a lot of our control villages are moving or something like that. That doesn't seem to be the case. And then, you know, we also... So we end up replacing these households. In the end line, we do another sampling of new households. And then the other thing we do is because our kids have aged out, you know, we're going in over two years later. And remember, we're interested in looking at these health impacts of 5 and a lot of our kids have kind of aged out of this group. So we also resample and get some more households with young kids in these same treatment and control villages. And so again, we need to make sure that these households don't look different than our households that are in the panel sample. And again, you know, I'm not going to show you all the tables right now, but we do find that the differences in characteristics of the new sample are not significantly different than our old households. Okay, so this is just to show you guys some data on, you know, where our treatment village is treated and where our control village actually controls. And so we have three different sources of data we use to measure this. So we have, you know, so the WFP data, this is actually the program guys who are going in and implementing the program. So they can tell us which programs they treated and which programs they didn't. So, you know, this is the first column here where, so each district, we have 10 treatment villages and 10 control villages. So according to their data, about 83% of our treatment villages were triggered and 4% of the control villages were also triggered. So there's 4% contamination here. But we also went in, you know, they're the guys implementing. And so in some sense, you kind of want an unbiased view of, you know, where who's being treated and who's not being treated. And so we have two different data sets. We have that longitudinal data set that I told you guys about where we were going in every few months to ask households. So if we look at that data set, we get that about, you know, 63% of our households in the treatment villages were treated and about 15% in the community villages were, sorry, in the control villages were treated. So we're getting both higher rates of contamination and lower rates of triggering in the treatment villages. And then we have another, you know, in our end line survey where we went in at the very end, we also asked households about whether or not they were treated. And there we get about 66% say they were treated and here we get about 14% remain control. So, you know, not bad, but clearly we don't have kind of 100% compliance here and 0% here. So they're, which again is, you know, in a real life program you would expect that. And then the other thing, this is just, you know, in this end line survey we asked households if they knew about triggering. And again, you'll see that, you know, there's not, this program isn't, it's not that, I don't know what the word is, not terribly strong and that only, you know, on average in our entire sample only about 15% of households report to knowledge of triggering. There's, you know, significant variation within each district. But you will see that it's not, you know, that say 100% of households that were in a village that were triggered actually even know about the triggering. Because part of this is, okay, the guy shows up for a few days in your village, but you might not be there that day, right? You might not even attend. And so part of this is also, in some sense it's a low exposure program. Okay, so this is our first, you know, before, okay, I go, I have until one, right? So before I get to any of these health impacts, you first need to know if there were any sanitation impacts, right? Because if I show you that there were health impacts, but there's no sort of first stage in that you don't actually observe anyone building toilets or doing any change, where these health impacts are coming from. And I've already told you that, you know, this is in some sense a kind of low exposure program. And so if we look, you know, our main, you know, sanitation outcome here is did you build a toilet or not in the past two years, right? And so this is basically, this top row here is that question. And so in our treatment villages, about 16% of households report to building a toilet in the past two years. And in our control villages, about 13% report to building a toilet in the past two years. And so the idea here that, you know, and then we just look at the difference in these two means and the difference, in fact, it is statistically significant. So households in treatment villages are about, you know, three percentage points more likely to build a toilet. And so that's about a 20% increase, you know, in toilet construction. So it's not big by any means. And, you know, it's fairly small, but I think given the program, it's sort of what you would expect too. And then what we do is we break this up. So by the way, these are just the numbers of toilets that were actually built. So then we say, okay, let's look at these households that built these toilets and break it up by people who actually had no sanitation previously, look at people who had unimproved sanitation previously, and then just people who had improved sanitation previously. So these are the guys who already have good sanitation but are just kind of building a new toilet, maybe because they're kind of the richer guys or they have the money to do it or whatever. And so here you'll notice that the greatest number of toilets are actually being built by people who already have improved sanitation. But the biggest difference across treatment and control is actually happening amongst those guys who did not have sanitation previously. So these are the guys, you know, in the treatment villages who are, you know, so here it's around 4% in the treatment versus 2.5% in the control. And this difference here is statistically significant, as is the one for guys who had unimproved sanitation previously. All right? Yes? So could you differentiate between no sanitation and unimproved sanitation? Mm-hmm. Yes, great question. I should have clarified that. So for improved sanitation we're using, there's this WHO UNICEF joint monitoring program definition of improved sanitation. And they basically say that it's improved sanitation when you have either a flush toilet or a latrine that either flows into a septic tank or a sewage system when it's either a ventilated improved pit latrine or when you have a pit latrine with a well that's covered by a concrete slab or when you have a composting toilet. So those are all improved sanitation and unimproved is basically anything else. And so in the context of Indonesia it might be. So a lot of people will have what they call hanging toilets where they'll build like an outhouse that hangs over the river so you go, you poo and then, you know, your poo goes straight into the river and it flows away. That would be an unimproved toilet. Or, you know, if you just have a latrine and the sewage is not going into a septic tank or kind of, you know, going somewhere where it should be. Okay. And so it is the case that, you know, a lot of people have unimproved sanitation but part of the program might be to then kind of get them to improve their sanitation by installing maybe a septic tank, for example. Okay, and then the other big sanitation outcome we look at is open defecation, right? So as I mentioned, the goal of this program is to get people to reduce rates of open defecation. And so here we also see some movement, you know, not huge but some movement. So in this first panel we look at everyone. This is everyone, this is control, this is treatment. And so what you can see is that men are now significantly less likely to defecate in the open as our children. So overall we're seeing this decrease in open defecation but it's mostly being driven by men and children and not the women. And then we actually, you know, separate this by river and not on river because as I mentioned a lot of the open defecation is actually happening when you have access to close rivers and you'll see that most of this change is actually being driven by these guys who live on rivers in that, you know, again, men are significantly less likely now to be defecating when they live near rivers as our kids. There's not much movement going on with the women we're not sure why but I think part of it is, you know, women go to the river as more of a social thing. They wash clothes there, they hang out there together and so, you know, because they're doing other things while they're there as well they might also be, you know, defecating but part of it is that they're still, they still have to go to the river to wash clothes and so maybe if they're going to poo they're still just going to go poo there, right? Whereas men and kids maybe now or it's easier for them to change their behavior. Just a matter of question. Yes. How internally valid is this question in terms of accurately relating to their behavior? You mean like because it's just a self-report and what should we believe with? Sure. I don't know. No, I don't know. Is there anything that people look at it? I know, I think that would entail some alterations but I mean I'm just... So, I mean the one thing I can tell you is there's actually not that much stigma associated with open defecation and so I'll show you in terms of attitudes towards open defecation. People aren't embarrassed by it because it's just something that they do. So it's not a social desirability of reporting. Exactly.  Exactly. So I don't think, you know, exactly. So in the context of, yeah, like sex and condom use you would definitely worry that people know you want them to say that you use condoms. Here I think it's less of an issue just because I think it's not that stigmatized but look, it's definitely... It is a self-report and so... And we didn't do any observational stuff for this. We do observational stuff for hand-washing which I'll tell you about in a minute. Quick question. Yes. So kind of on that end, you said you took long-term data and you also took, you know, snapshots of data at the end and the diarrhea rates you guys have got... Were those self-reported as well? Yes. Okay. Now, because open defecation may not have a stigma with it but diarrhea may. So did you find... Yeah. So I'm going to spend a while talking to you about our diarrhea results and whether or not they're real and we should believe them and this will be one issue that I will get to. But you're absolutely right. The diarrhea is also self-reported. Any other questions? Okay.  I should note that hand-washing and drinking water, drinking water definitely not part of the program at all so we shouldn't expect to see any big differences by treatment and control. And hand-washing too, it's not a huge part of the program.  And so we did collect all that type of stuff and, you know, and if you ask people if they wash their hands after they go to the bathroom, 98% of people say they do. And then, you know, but for some of this we actually had. So they're, you know, and I think part of this might also be this issue that Tim brings up that everyone knows you should say, you know, that you wash your hands after you go to the bathroom but not necessarily everyone does every time. But we did have our enumerators go and actually look at hand-washing facilities. So, you know, we would say, do you have a hand-washing station? And a hand-washing station is just somewhere where you wash your hands after you go to the bathroom. And then if the household would say yes, the enumerator would actually go and say, can you show it to me? And they would look to see if there's soap there and if there's running water and what kind of stuff is there. And, you know, and water was available in most of the stations. So in less, you know, again, which is what you would expect. But there's no significant difference in hand-washing behavior or hand-washing infrastructure by treatment in control villages. And so, again, you wouldn't expect, if you see any differences into these health outcomes, it doesn't seem to be the case that it's because hand-washing is driving it. And the same goes for drinking water. Drinking, you know, interestingly, in rural Indonesia, households actually have pretty good access to good drinking water. So these guys aren't drinking water from the river.  And, again, we don't see any differences between treatment and control communities in drinking water. So, again, you know, if you see differences in health outcomes, it doesn't seem to be the case that it's because of access to drinking water. And then here, you know, we have, this is, again, getting at some of the issues of attitudes towards open defecation. You know, how do people feel about open defecation? And we don't see, I think there's no, yeah, there's no significant differences across treatment and control communities. So this, we asked them at the end line, after the treatment has already happened, and you see that there's no big differences in how people feel about open defecation. And you'll notice here, you know, that around 30% of people strongly agree or agree with the fact that it's acceptable to practice open defecation if you don't have a toilet or a train. But they also do seem to know that if you do have a toilet or facility in the village, it'll benefit the community, as environmental pollution will be lessened. You know, most people I know normally defecate in a toilet latrine. And so it seems to be the case that people do understand, you know, that defecating, that open defecating is bad or unhealthy, but that it seems to be fairly okay if you don't have a toilet or a latrine. What else? So we also asked Caregivers' Perception about causes of diarrhea to see, you know, is it the case that our treatment village is just no more about causes and perceptions of diarrhea? And this, so here we actually asked this question in the baseline as well as in the end line, and so we're able to do a difference in difference. And again, you'll find that in general for most of these, there isn't any significant difference in Caregivers' Perception about causes of diarrhea. Most people seem to have a fairly good sense about what causes diarrhea. The only two significantly different measures here, our treatment villages do seem to know that drinking unclean water is a cause of diarrhea. But if you believe this result, the treatment villages also think that exposure to sun is a cause of diarrhea. So these might just be kind of spurious results, but in general it seems to be the case that the treatment and control guys know about what these, you know, they have the knowledge, and I think they probably had the knowledge before this treatment even went in. In terms of, you know, at the end line we said for those guys who didn't build a latrine, why didn't you build a latrine? And, you know, close to 50% of households report the high cost. And so this, you know, we've sort of been pushing this result to WSP as a reason in terms of why they might actually think about, you know, giving these households some money to build a latrine because in fact it's not very costly. So WSP estimates that it costs between 50 and 90 US dollars to build one of these, you know, toilets, which isn't that much money. And what's interesting is we asked in the end line, we wanted to get a perception from these households like how costly they actually think it is to build a latrine.  So it does seem that households believe it costs a lot more than at least WSP estimates. It also, you know, some of the districts where we saw more construction or sort of more successful construction were providing access to cheap credit. And so this was happening, you know, within the community where community members were getting together and doing these sort of rotating savings credit unions where different village members all contribute and then every month someone gets to take on the pot of money. And so that was one way that villages dealt with the fact that they might have credit constraints and not be able to build a toilet. Okay, so let me, in the last 15 minutes I have, let me now move on to some health outcomes. So just to kind of summarize, it looks like we've had, you know, some sanitation impacts that have been significant increases in toilet building, significant decreases in ODF, but they haven't been that big, right? They've been, you know, okay. So let me show you now. We have diarrhea. So this is inline data treatment and control. So treatment is green and control is red. And when you see stars, that means that there's actually statistical difference there. So there's no significant difference between RRI and ARLI for treatment and control. But you'll notice that in the inline data, we do see that our treatment villages are significantly less likely to report having diarrhea prevalence in the last seven days. And we actually use, so the WHO, they have sort of symptom based measures of diarrhea. So we're not actually asking households, did your kid have diarrhea in the past? We do this for two days, seven days, 24 hours. We'd have various measures of diarrhea. But I should note, we're doing this, we have this sort of child health calendar of health things. And so for diarrhea, you get that you had diarrhea if you either report to having your kid having three bowel movements in the last 24 hours, watery or soft stool, or mucus or blood and stool. So these are symptom based definitions of diarrhea. And you'll notice that in all three of these, our treatment guys are less likely to have three bowel movements, they're less likely to have mucus or blood in school in the stool, and they're also less likely to have watery or soft stool. The mucus or blood and stool is the only statistical difference, but the data looks the way it should if you believe the diarrhea results. So we did it similar for ARI where it's all symptom based, but there's not much going on there. And then we also, we just wanted to have some sort of placebo effect health outcomes, right? So you wouldn't expect to see anything happening with skin itching from probably from a sanitation program or like abrasion, scrapes or bruising. And you don't actually see any significant difference going on there. Good question? Yes. The last one? Yes. I mean, looking at that, I mean, it doesn't seem like there's...  So let me show you the regress. So these are all means right now. I'm going to put all of this into regression analysis and then basically what you're going to see is that the only result that actually holds up is the diarrhea stuff and this will go away. And so then, but you know, just, we look at all these other health outcomes, right? So we look at anemia, hemoglobin, weight for age, height for age. And these are all just means, by the way, but there's just nothing going on, right? So the, you know, in terms of just looking at means of all these different health outcomes that we've measured, the only one that kind of sticks with us is the diarrhea result. But then we say, well, let's actually put this all into, you know, regression format. And so we're going to estimate these intentions to treat regressions. And so the idea here is we're just going to estimate the program impact off of a comparison of those communities who were treated relative to those communities who weren't treated. Okay, so it's just going to be this sort of average impact. And our Y is going to be our health outcome of interest or sanitation outcome of interest. T is just going to be a dummy for whether or not your village was treated. So we'll run this first, which is, you know, and we'll put in, you know, sub-district community fixed effects because that was the level of our randomization. And then we're going to cluster all of our villages, all of our standard errors at the village level. Again, because we randomized it at the village level. And then in other specifications, we'll also put in, you know, baseline controls for baseline and other different control variables. And so when we do that, you know, this is the, this is where the, so each of these is a different Y. So this is the dependent variable for when you're, you know, if you built a toilet in the past two years. And really it's kind of the strongest, the red means that it's statistically significant. And so all these other sanitation measures, it doesn't seem like that much is going on. And I should note that each column is just a slightly different specification with different, you know, sometimes we control for baseline characteristics. Sometimes we include other controls. Sometimes we have fixed effects. And it's just, you know, to kind of show you how robust these results are. And the big robust result seems to be that, you know, treatment villages are significantly more likely to build a toilet by around three percentage points. And that's about a 20% increase from the mean. The ODF result, remember that I showed you the means where we had differences, but once we put it into regressions, we're actually losing statistical significance. The only ODF result which we get is, you know, if you are on a river, you're less likely to engage in open defecation. And hand washing behavior, again, we're not getting much and we wouldn't expect to get that much because it wasn't a big component of the program. So here's our diarrhea. So as I mentioned, we have, you know, two-day diarrhea prevalence and seven-day diarrhea prevalence. And this is where, and then this is the ARI and ALRI. And again, you know, you'll notice that the diarrhea results, they're there, they're pretty strong, and they're pretty big. So the seven-day mean of diarrhea is around 4.6%. And so this is basically saying that, you know, if you're in a treatment village, you're 30% less likely to report to, you know, your kid having seven-day diarrhea prevalence. And then the two-day result is even bigger because the mean there was about 3.4. So this is about a 40% decrease in two-day diarrhea. And so the, and so let me just say, so we've had these sanitation impacts which are not that big, this diarrhea result which is pretty big. And so what we're, you know, what we've been spending time thinking about is, you know, are these results real? How much can we trust them? And so I want to spend five minutes now talking to you a little about what we're doing there. And I guess, you know, before I get to that though, I should just quickly show you that in terms of all the other different health outcomes we have, there's not that much going on with the health outcomes. We do, you know, we do see the mucus or blood and stool, the refusal to eat. All of these things you would imagine are probably correlated with diarrhea. There is something here a little bit weird going on with increases in congestion or runny nose that I'm not sure, you know, what is exactly going on there. But again, none of the, none of the high weight, anemia, cognitive development, nothing is going on. So we've, the only real big health impact we've seen from this program, if we believe it, is the diarrhea. And so the, you know, the important question I think that you guys have been pushing me on is these are self reports of diarrhea. How much can we trust them? And so as I mentioned to you in the end line, we did collect fecal samples. And those are actually being analyzed right now in the lab. So we have like half the data. We don't have the rest of it. And so, you know, if we were able to kind of corroborate the diarrhea results where we are finding that the fecal samples look a lot better for the kids in the treatment villages, then I think, you know, you, again, you'd be much more likely to believe these big diarrhea results. We've also looked at, you know, medical usage. So is it the fact, again, you know, you're seeing these big diarrhea results. So do we see kids in these treatment villages being taken more to the doctor? Or are they actually getting more treatment? And so what we do is we regress the medical clinic use on a treatment dummy. And we do actually find that the diarrhea families in treatment groups are significantly more likely to actually use medical clinics when their kids have diarrhea symptoms. So, you know, that's one thing that sort of corroborates the story we're telling. We're also, and this is something that we're doing, we have, you know, another reason you might see these big diarrhea impacts if they exist because of this externality issue, right? It could be the case that, sure, not tons of people are building toilets, but if there's sort of this multiplier effect of this sort of externality of people, you know, that having, you could observe these big decreases in diarrhea due to externalities. And so we have GPS data on households, and so what we're, we're kind of, we're basically looking at things, you know, like distances within households, distances to river. And we want to see if we can use some of that to actually explain these big decreases in diarrhea. And then the last thing we're doing, which I'll show you quickly, is we decided to look within districts, right? You could imagine that there's a lot of heterogeneity within district in terms of, you know, how people build toilets and how they behave. And so what we want to do is we want to look in the districts where we see lots of toilets being built, that's where you would expect the big diarrhea decreases to come from, right? And if you're observing big diarrhea decreases coming from districts where no toilets were built, then again, you might think that's a bit weird and maybe these diarrhea results are just furious or something. And so we do look at that, you know, so we look at each of these as a district, and we have, you know, our red is our treatment and our blue is our control. And, you know, I guess the only district where we're getting, where the difference, the increase in toilet construction here is significant, is Mario. But remember, these are small sample sizes and it's, you know, doing statistical tests with small sample sizes within districts.  We do the same thing, you know, for ODF. And then what we do is we just, we generate this table, right? And so we say, okay, in Zhongbang, you know, we do see open defecation going down, nothing much going on with diarrhea. In Blitar, we see open defecation going down, diarrhea is decreasing. And so we do that for each district. And in general, I think most of the districts look how you would expect. Banyuwangi is the one weird district where there's no significant sanitation improvement, but you're seeing big decreases in, in diarrhea. And, but though it's not significant, you know, if you look at Banyuwangi, it does seem to be, you know, it is the case that more treatment villages are building, treatment households are building toilets. It's just that the difference isn't statistically significant. And so again, you know, it seems that if you look kind of within district, we are getting results that we would expect. But I think the big, the big open question and the big thing that will sort of help us solve this question of whether or not the diarrhea results are real are these fecal samples, right? Because the fecal samples, they're not self-reports. And so, you know, for, I guess, for, you know, if you're worried that all the people are misreporting their diarrhea symptoms, for example, we hope that, that that wouldn't be an issue with the fecal samples. So in reference to the stool samples. I hope you're not going to ask me which parasites they're testing for. Goodbye. I was thinking I should write this down, but I... It's all right. So, I mean, stool samples are, it's really temporal. I mean, if you take a snapshot, you know, one day to the next can be completely different. So I guess, you know, my thought in it is that that information would be almost worthless. Really? You're taking one snapshot. And I mean, whether, and depending on the location, depending on whether or not it's rained within the past 24 hours, there's a whole host. And also because you can't test for the whole slate of everything that it gives you sick, it just seems like there's so many variables in there that one sample before and after can't really tell you anything.  We are people who are consulting on this project to know much more about like health and parasites than I do have helped us think about, you know, which things they should be testing for, which I could definitely get you information on. But again, you're absolutely right that it's just one snapshot in time. And at the end of the day, that's really all that we could pay for.  So it's, you know... And there also, I mean, on average, would there be raining in the treatment communities and dry and control communities? Oh, no. That's the issue, right? So it's going to be a ton of measurement error and a lot of confounders to the extent that it's not contingent upon any geographic, you know, if your selection was unrelated to the geography and unrelated to these factors. You know, that's the only potentially... I mean, that could imagine that also. But you also talk about externalities of the link toilets. Would that occur if a more than one individual, one household uses the same toilet? Or if you have, like, a super-shutter deciding to use the toilet? So the one who actually ended up infecting everybody else decided to own up and get the toilet. And how would the data actually differentiate if it goes in a variety of other externalities? It wouldn't. I mean, I think all we're going to be able to do or all we can really look at is this sort of more average externality issue, but we won't be able to disaggregate between story one and story two that you just pulled. Do you know which one might be more compelling? I mean, based on what's happening in those communities and I'm being close about that. I mean, we could, you know... Is there any kind of communal notion of a toilet? Or is it really like a personal property?  It's definitely much more of a personal property. People don't want community toilets because there's this whole issue of who's going to clean it and they get dirty.  I think there is maybe a little issue of their sympathies associated with having your own toilet. So I would say for the most part it is private use toilets that people are building. In a few cases, though, you do have, oh, we share our toilet with our neighbor because they're like close friends and, you know... And so then you get rid of some of these pre-writing issues that community toilets interface. Were there still samples from every child? Or only those with diabetes? No, every child. Every child under the age of five within a household. So what was the age that you were looking for in children? Oh, so it was... We have all the health stuff is for kids under five. Okay, kids under five. Yeah. So I mean, one thought is that you could... They're under five, so maybe the others are probably some absolute... But you could look at school attendance to try to back up. Yeah, so exactly. So we are... These are things that we're also looking at things like we have things on school attendance, how much time maybe moms are spending caring for kids, is that changing, you know, labor market issues. So we're absolutely... The last thing is, when you showed that the graph of, you know, when you break it down by district, you know, there were two districts in particular that had significant gains. Did you guys look into what sort of maybe secondary factors went into them adopting improved sanitation? Maybe a baseline they had, or maybe they have, you know, a high percentage education. Maybe they have greater financial resources so that, you know, given when they realize, oh, LaTrinck really helped, you know, they actually have the resources to put them in place. Right. I mean, we haven't yet, but we... It's definitely on the list of things to do in terms of, yes, someone was telling us an interesting paper would just be... Exactly. Like, why are some districts successful when others aren't? Yeah, because then you could really tailor an intervention directly to that.  I mean, you could say, well, you know, this group has an average of $60, $70 income, you know, per person per year, and maybe you could help direct the intervention that way. Absolutely. Yeah. Tell me a question, would it, or do you want to finish? I can keep taking questions. I'm basically almost done, so... Yeah, Ben? Well, I have a quick question that might help with the difference between the communities. Yeah. Different facilitators were there. Oh, so that's a great question, right? So we... Because you're absolutely right in that you could think that facilitators are going to drive success and that if you had, like, a really charismatic guy coming into your... You know, because we actually observed a lot of the interventions, and it was true that some of these guys were great, and I thought, God, I would build a toilet, and then others were sort of like... And so we collected information on these facilitators in terms of, like, how charismatic do you think he was, and we haven't actually looked at that data yet, but that could also be one of these things predicting, you know, why people are building toilets versus not. Because we did... There were a lot of different facilitators, you know. I mean, every district has its own set of facilitators. It's not that these guys are going, you know, all across the province or anything. Jumping out that curious question, how many... Was there, you know, a ratio of, you know, facilitator per population? You know, because obviously going and talking to 100 people to close to 1,000 won't be a lot more effective. Yeah, yeah, yeah, no. There were something like 25 to 30 facilitators across the province. But I don't know. I mean, I could easily find out, though, you know, how many were in each district, for example. I just know the province number. Mm-hmm. I was struck by the difference between access to improved drinking water and lack of access to improved sanitation. Mm-hmm. Typically, they go ahead and... Yeah. So I assume that the drinking water is communal and it's chlorinated and so people go out with their buckets to a central location. And that's... It's free to them because it's provided by the government. Yeah, so the Indonesian government, for some reason that I don't know why, there's been lots more water stuff happening than sanitation stuff. And so you're absolutely right that water is not... Bad drinking water is not a huge issue in rural Indonesia. Because running water, some scholars have said that's the most important thing in the area for them. Mm-hmm. That's not supposed to whether you're using a pit latrine. If you have a pit latrine and you can push your hands, you're much better off. Right. And you'll notice, I mean, no one's raised this, but the diarrheal prevalence in East Java, actually, we were surprised at how low it was. I mean, it wasn't that high. That's not true in other provinces in Indonesia. You could go to poorer places where you would observe much higher rates of diarrhea. And part of, I think, why WSB decided to roll out the program first in East Java is because it's just an easier place to work in terms of the government actually functions and institutions function relatively well. And in some of these places where diarrheal prevalence is a lot higher, these places are also a lot poorer and it would have been much harder to roll out the program, which are things that they're going to have to deal with now, though, as they actually do go into these poorer rural areas. Any other questions? I guess I'm over time, so I should probably wrap up. But, yeah, I think I've, you know, said everything I needed to say, so. Thank you.