Hi, this is Dr. Don with a problem out of Chapter 2 in McClave and my stat lab. This one gives us some raw data and asks us to find the range, variance, and standard deviation of this sample. And they're asking for descriptive statistics. We get those very quickly using stat crunch. Click on the rectangle there, open in stat crunch. So we have our data already there. We just need to go to stat, which is the place you normally go for most things in stat crunch. This is summary statistics, which is the same thing as descriptive statistics. And our data is in columns. So we select that data. Just click on it to move it over to the right side. And as we go down, we need to have the variance, so I'm going to click on that to select it. Scroll key, and then move down. I need the standard deviation and the range. Of course, you can get all these other statistics if you need them, the means, standard error, median, min, max, the intercourtile range. So just be aware of that, that that summary statistics option in stat crunch will give you all of those things. Here we get very quickly our variance 4.86, which is that answer, range of six, which is that answer, and the standard deviation of 2.2, that answer there. Very quickly done in stat crunch.


School may be over, but one project is still ongoing. Hedy Clotter shows us how the Gene Dillon Elementary School is coming along. By next year, this construction site will be the new Gene Dillon Elementary School. The multiple-story building will serve fourth and fifth graders and is set on 160 acres, which will include gyms, playing fields and parking lots. Crews have been working since May 10th and are already making progress. Now it's June 5th. I would say, as far as footings and foundations, we have to be about 70 percent done. The sewer and water utility is in and so are the storm drains where water will run into the storm structures and into the ponds. The playing fields are also ready for seeding. Cons may scenario, they're working on the south wall of area C. They'll get it up to as high as they can reach before they set scaffolding. I'm standing inside the future home of the main gym in area A. The crew is using tower scaffolding to make it easier, faster and safer to lay the concrete block. Safety is key and no one is allowed on the site without proper gear. A reflective vest, safety glasses and a hard hat. Between the big steel, going up the structural steel, that would be our next big thing to start. Lead superintendent Gary Francisco says the project is going well and ahead of schedule. I have some hurdles along the way, I have no doubt, but we will hurdle them and we will be done on time. In Bemidji, Haiti quarter, Lakeland News. The school's date of completion is expected by the beginning of the 2018-2019 school year. If you've enjoyed this segment of Lakeland News, please consider making a tax deductible contribution to Lakeland Public Television.


I am extremely grateful for the training that I received at the University of Maryland to an adolescent fellowship program. My co-fellows are amazing. We make each other laugh and we give each other support. I really appreciate them and don't think I would have made it this far without them. I made lifelong friendships during fellowship. I look back and I think about how we made it through training together and marriages and raising children. These are friendships that will be around forever. A lot of emphasis was placed on teaching and mentorship. And I had several outstanding female role models to look up to. The fellowship was really great for the lifestyle that I wanted to have.  The fellowship, it helped prepare me to handle every form of psychopathology. I don't think you can get the amount of training experiences at any other program. I've been in Baltimore now for 13 years and love it here. There's a lot going on. There's lots of restaurants. I really do like the diversity of the city. I really enjoyed the fellowship. I think it's one of the best things I've ever done for myself. And I feel like our work matters and makes a difference.


One more person may be coming from Quebec to join us here in the Philippines. You know who you are Chris at this rate We may need to call this Quebec in the Philippines There is another person from the UK with letters JJ who told me he would love to come here to the Philippines After seeing all these videos behind me of how life is like where we are living and working in Eastwood City Will you join us next? There is a yet another person who told us that his name begins with the letter A From the United States that may be coming very soon to the Philippines. Will you be next? my Personal goal is to have 100 people from abroad Come here to the Philippines to live and work with us with me with the team with Everyone at freedom who is headquartered here in the Philippines? We have an office and we have 20,000 square feet of office space that we're moving into in two months to expand We have potentially a school. That's two That's cool. If it starts we'll start with preschool where we will invite everyone whose Pre-school to come study at our center. It will not be called the George Manuse Institute of International Learning Most likely that was a suggestion by Brita if you look behind me That's his comment where he suggested that name. Thank you, Brita. You rock for making that suggestion The Philippines is a lifestyle. This is not just working for freedom. This is not just building the best Network on YouTube. This is discovering who you are By exploring a new culture where you can focus on finding new friends building new Relationships to really find out who you're going to be in life One of my personal goals is to help people Discover themselves because I discovered myself when I came to Taiwan Back in 2008 as some of you know, I decided to leave North America Get on a jet plane and head on over to Taiwan Because I had the idea of promoting Asian singers outside of Asia who sang well in English in Taiwan I met my future wife Shaoyu and that changed my life I think many of you may be looking for a special someone and I think many of you are looking to build your career. I Hope moving to the Philippines will help you accomplish both goals Our second recruit Doug aka Sata has told me he is the happiest He's ever been because he came to the Philippines. He's been to the US He's from Australia and he's done a variety of jobs But he's never found something that makes him so happy as now And I hope he'll make a video talking about that from his own personal experience I would love to have you join us If you think you can be more productive here in the Philippines If you think you can meaningfully contribute to helping freedom grow to helping yourself grow and to helping Everybody grow together. If you think so then head on over to our new jobs site Jobs.freedom.tm which is linked down there in the description below That is where you can apply To come to the Philippines by submitting your plan submitting who you are Professionally so we can make a better decision. My goal is to get a hundred of you over here in the next in this year 2015 can you be part of that 100? Let's find out Who am I? I am George CEO and founder of freedom and you've been watching the George Grades if you really look at yourself you will be speechless What happened to being doctors and teachers? What happened to being your brothers keepers nowadays?


Productivity is what determines your success in life. It is your ability to produce an outstanding result upon completion of a task. Danez Kennedy, a strategic advisor and business coach, defined it this way. Productivity is the deliberate strategic investment of your time, talent, intelligence, energy, resources, and opportunities in a manner calculated to move you measurably closer to meaningful goals. Contrary to what majority of people think, productivity is not your ability to get things done, but your ability to accomplish your goals and produce exceptional results. Paul J. Meyer, the pioneer of self-improvement industry, said, Productivity is never an accident. It is always the result of a commitment to excellence, intelligent planning, and focused efforts. Also, Tim Ferriss, an American entrepreneur and author once advised, focus on being productive instead of being busy. Again, productivity is not measured by how much work you spend your time doing, but what comes out of it. So, if you spend 24 hours working on a project with no results to show for it, then you've just spent the entire time being busy, not necessarily productive. Jim Ron said, You don't get paid for the R. You get paid for the value you bring to the R. To become more productive, you have to be proactive and intentional in taking actions that can help you accomplish your goals. In this video, I'll share with you 9 smart hacks for maximizing productivity. If you're new here, consider subscribing so that you don't miss out on exciting videos like this. 1. Make your to-do list a night before A productive day starts the night before because you know what you intend to spend the entire day doing. You know the results and goals that you expect to accomplish by the end of the day. Also, your productivity is highest in the morning, so it'll help to know what you want to spend the morning doing instead of using the time to figure it out. So use your peak productivity to perform tasks and not to think about the task to be completed. Also, about the importance of creating a to-do list, Richard Branson, CEO of Virgin Group said, List not only provide great structure to getting things done, but it also help us to set goals and achieve our dreams. 2. Set time for every task Once you have set up your list, put time boundaries to every task on that list. When you place a time bound on a job, you stand an excellent chance to get it accomplished on time. However, when you don't, chances are you may never achieve that goal at the end of the day. Here's the thing, when you don't set time to a task, you'll spend the day believing that you have enough time to get it done eventually. When you set time boundaries, however, you most likely complete all the tasks that you set for the day. 3. Focus on one task at a time Susan Cain, an American writer, said, What looks like multitasking is really switching back and forth between multiple tasks, which reduces productivity and increases mistakes by up to 50%. When you multitask, your brain focuses on one of the activities and quickly changes that focus on another task when you switch. So, you can only be reading or you can only be watching the news, but not conveniently do both. If I look like multitasking helps get a lot of tasks done in no time, however, if you want to get an exceptional result, then it's essential to learn how to focus on your task one at a time. 4. Focus on high priority tasks first Stephen Covey said, The key is not to prioritize what's on your schedule, but to shadow your priorities. And according to Dale Carnegie, do the hard jobs first, easy jobs will take care of themselves. It's best to start your day carrying out the most critical task on your list. Do the high priority jobs first because your productivity is maximum at the beginning of the day. So, start with these tasks before your brain gets tired and distracted quickly. 5.  Skipping breaks, on the other hand, leads to stress and fatigue. When you work hours upon hours without taking a break, you will eventually become tired. Your productivity level will drop and at the end, you might not accomplish your goals. You can work for 90 minutes, then take a 15-minute short break to freshen yourself. Go for a quick stroll outside your workplace. And if you can, take a 10- to 20-minute power nap in your lunchtime to replenish your energy. Please, try not to make the mistake of logging into social media or answering your phone during your breaks because it can turn your 15-minute break into a 15-minute break. 6. Put your phone away When you're working on something, the sound of your phone notifications can serve as distractions, quickly destroying your concentration. So you can switch off your phone, put it in airplane mode or put it away in another room where you can only hear the sound of your ringtone and text message notifications in case of a work emergency. That way, you'll reduce the interruptions that can arise from checking your social media or email notifications regularly. 7. Listen to audiobooks and podcasts while eating or commuting Think of something that you love doing and find a way to learn more about it. When you are eating, commuting, or doing any other routine activity that doesn't require the active use of your brain, listening to a podcast or audiobook can be helpful. The idea is to go for something that you want to learn but still enjoyable and beneficial to your life and work. 8. Learn something new during your free time Productivity requires you to keep learning. You can't keep doing the same old way every time, otherwise sooner or later it will become impossible to achieve a goal because you are not improving yourself. So, instead of spending your free time watching TV or on your mobile phone, invest in learning. You can pick up a book or sign up for an online course that can be beneficial to you. 9. Create a good workspace The last thing you want to do is set up a workstation around your friends or family members because you will end up using that time as chit chat time instead of work time. So find the right location and the most convenient position to help you get work done. You can lie on your bed and expect to get your job done in good time. You have to create a great space with the right materials like a desk and chair, table lamp, bookshelf, etc. There you go. Now you can work on becoming more productive. Thank you very much for watching our videos. If you like this video, watch more videos on our channel and subscribe. We love you.


Can you see my screen? Yes, and I'm recording now, OK? Sure. We can see your screen. OK, good. All right, so good morning, good day, good afternoon, ladies and gentlemen. I welcome all of you to this session on SSI and Biometrics, Building Identities for Tomorrow. I'm the Jani Mohanty. I work for an organization called Earth ID, and where we are building identities, decentralized identity, and biometrics-based applications. So like already said, that I'm a blockchain author as well, and my latest work is blockchain for self-server and digital identity. And so in today's session, I'll be covering mostly the biometrics-based authentication, different type of biometrics, with their pros and cons, and the issues in the biometric solutions. I won't go to detail out SSI because I believe that most of you are aware how self-servering identity works. But I'll just touch base upon it so that you'll know that how these two technologies can work together, and come up with the Earth ID solution, few of our solution. And then we'll discuss some use cases which are either in production or pretty close, and with some innovation in this field. And finally, if time allows, then we'll do some question and answer session. So first of all, I don't think that any of you have any doubt that biometrics is used for authentication. Today, organizations identify a person from their name, address, phone number, or email ID, which can change. So with such information, one person can create many identities at a time. So you might have heard that a person has got five different passports and a fake passport. So why it is possible is because we use such kind of information, and they are not unique. Also, there might be a complex process to update the data associated with your identity. For example, pretty recently, I had to update my address on my ADHAR, which is India's national identity system. And it really took so much of such a lengthy process and pretty complex. However, if biometrics is something which represents your identity, then it is very unique to a person. And biometrics is the real you. You are actually carrying your biometrics along with you. So it won't lead to hassles like such. Perhaps that's the reason why that global biometrics market is expected to top US $50 billion by 2024. So let's start with biometrics. So currently, most of the applications in today's world, people are moving towards a passwordless world. Why? Because first of all, user ID and passwords are hassles. You have to remember them. And many people use the same user ID again and again. Maybe their email ID or contact number. However, the biometrics are forever. Most passwords are very easy to guess, but you cannot guess somebody's biometrics. Resetting the password is a process that you have to do again and again, but with biometrics, you don't need it. Like I already said, user ID, mobile number, email IDs, et cetera, can change. However, the biometrics doesn't change, especially. I mean, for a very long time period, maybe you can say, from a kid, from a baby to adulthood, biometrics might change a little bit. But again, it stays with you for a very long time. Hackers can steal the user ID and the password through different social engineering technologies. But it's difficult. I mean, I won't say that biometrics can't be stolen, but it's actually difficult and much more expensive. And often, you are using, like I already said, the same user IDs are used again and again, the same email ID or same contact number you are using for multiple applications with multiple different organizations. So it's easy to trace back the user because you are using the same user ID ever. But so this is called correlation, which is pretty much possible with user IDs, but with biometrics. It's not that easy and very difficult to count of it. Now, coming to how biometrics works. The biometrics actually censors scans the user's biometric data, such as the fingerprint, face, palm, et cetera.  There's so many different things that you can scan with a scanner. And it performs some pre-processing to enhance the quality of the captured biometric data. And some features as extracted, from which the mapping is created, our mapping file is created. And from this mapping, a template is created, which is a binary format. So usually people just wonder what is a biometric template? It looks like this, just one and zeros, binary format, which you can save to a template database. So this is known as the enrollment phase of where the biometric grid is captured and stored in template database. So basically biometrics, just like user-read-in password, it has got two different phages. So with your user-read-in password, first you register and then you log in. Similarly, the first phase with biometrics is enrollment, where you are providing your biometrics to the system and it's getting saved in terms of templates. At a later point of time, when you want to log in, which is called recognition, your biometrics is again captured and again the template is extracted and those two templates are compared. And for every system, there is a threshold value. If the matching is more than the threshold value, then we consider it as a match, otherwise no match. So... Can I ask a question? Yeah. Why should we store the image if only the template comparison is done? I'm sorry, the image is not stored. It's only the biometrics, the template is stored. Maybe you know... Because in your slide, there is something called an image archive. Okay, so... Yeah, maybe it's a little misleading. I'll update this. So actually, we're not saving the image, but only the template. Beautiful, thank you. Okay. So one major issue over here is, while we try to find out what kind of biometrics that we need for your application, one major factor is biometric effectiveness, which is the false acceptance rate and the false rejection rate. So the false acceptance rate measures how lightly it is for an unauthorized user to be incorrectly given access. That means I'm not rightful on it, but still I'm getting access, which is really, really crucial for application not to go with it. The second one is a false rejection rate. That means I'm the right user, but I'm not getting access. So this major is how likely it is for an unauthorized user not to get an access or denied access. For the authentication system, especially, these two rates should be extremely, extremely low. And there are other issues also in biometrics, like first of all, just like your user ID and password, the biometrics can also be stolen because a database that stores the biometric data can still be hacked. This is mostly done in a centralized way, so this can be stolen. The second one is injuries. So injuries can cause a biometric authentication not to work like if you have a burn on your finger, it could lead to a negation on a fingerprint scanner. Third is false rejection and false acceptance rate that I just discussed out. Then some systems are harder to adapt for elderly or those with having disabilities or maybe babies. So discomfort is another area. And finally, it is relatively expensive than the user ID and password. So there are so many different issues that we need to consider before choosing the right kind of biometrics for our use case. Now, there are different type of biometrics. Broadly, they can be categorized into physiological and behavioral.  So basically, we at R3D are mostly working with physiological, but there are certain behavioral biometrics that are under research or you'd be working. And I'll come up with some use cases why we need behavioral and why behavioral is going to be used in many future use cases. So why I'm actually discussing so many different kind of biometrics type because there is no particular biometrics which is considered to be the best. There are different, there is no one size that fits all over here. There are different use cases and for different use cases that could be different kind of biometrics that we need. So currently I'm going to discuss a couple of biometrics that we are using at R3D and I'll compare them on the basis of these factors, security, accuracy, privacy, ease of use, health and hygiene, cost, how cost effective they are and what are the exclusions? That means that who are the people who have to be excluded for this kind of biometrics? So let's explore them one by one. First of all is fingerprint. So fingerprint, of course, it's a very secure biometrics and it's a proof solution with the history of past almost 100 years. A's of use is very high, your fingerprint are always with you and it is also very cost effective because there are so many, you might have seen so many fingerprint scanners everywhere and this is a technology which is being used highly for the governments and many private sectors are also using it. However, coming to the disadvantage, number one is we leave our fingerprints on almost everything we touch. So making it possible for them to be collected without our consent. So you can visit YouTube and you can find how easily one can create a fingerprint mold and they can use it against a scanner. So really easy for hacking. Also, if we consider the health and hygiene, especially in the COVID time, this biometric pattern requires a direct contact with the sensor to identify the user. So it creates a hygiene concern when a scanner is shared among a large number of people. Then exclusions. So while fingerprints remain relatively stable over a person's lifetime, there are sections of this population which can be excluded using the system. For example, old people and certain diseases, people with certain kind of diseases might not use it because their fingerprint might have been worn out or less accurate to be compared. In babies also, the fingerprint is less developed. When I tell you that accuracy is slow, you won't believe because no fingerprint has been used for such a long time, but it is accurate. But then if we compare it with others, like if we see that what are the FRR, false rejection, false acceptance rate, then fingerprint is relatively less than a couple of others which I'm going to discuss out. So the next one is facial recognition. So again, in a health and hygiene is very high over here because you are not going to touch anything. You can do it from a distance and also ease of use is very high because you can do it even from your mobile phone. So this doesn't need a special camera. So ease of use is very high and there are so many apps today that you can run on your mobile device and they can figure out who are you from this official calculation. At least like I said, it is cost-effective because you can do it with a ordinary camera in a smartphone. There is no such exclusion. You can do it on babies. You can do it on elderly's. So on a digits person, so facial recognition works for everyone. And also this is a very fast technology. The process of recognizing a face text a second or less. So that is why it is used widely across airports in the world for matching a picture in passport. When I come to the negatives, first of all privacy. So with the help of this technology, the government can track down the criminals but at the same time, it can actually track down people like you and me anytime and anywhere. So that is why in many countries, especially in Europe, citizens have raised their concern not to use this technology. Second one is accuracy. How accurate is facial recognition? Actually, this is not that accurate. So two people can look very similar. What about identical twins? They look very similar, right? So that is why it is not that accurate. And also the facial features change almost a couple of years. So that is why you have to keep on updating your biometrics from time to time. Also the false acceptance and false rejection rate can be pretty high with this. So of course, considering the privacy and accuracy are low, so security is also low. Third one is retina. So many people actually are not able to distinguish what is retina and what is iris. I will come to the iris in the next slide. So iris is the part of the eye that you can see from front. Whereas retina is the backside of the eye where the image is getting formed. So because this is a private part, so that is why it is extremely secure. Nobody can take a picture of your retina. This needs a special camera and it cannot be done with a regular camera. It cannot be done with your mobile device on your smartphone. The accuracy is very high. So extremely low false rejection rates close to 0% and low occurrence of false positives. So and also like I said, it's highly secure, highly accurate and highly private. So that is why it is being used in places, not on like mobile device, as I said, that it needs special camera, but it is used in places like FBI or NASA to get access to certain areas. Now, the negatives are first of all is of use because it uses a special kind of the camera and a person has to focus to a point for almost 15 seconds without moving their eyes. So and some people have a feeling of a temporary blindness after the retina scan. So health and hygiene considering, this is a contactless technology, but again, not for everyone. And exclusions cannot be used for people with cataract or few other eye diseases. And like I said, health is also a problem. And also this is highly expensive because you need a special camera. You cannot do it with regular camera. So it is expensive and not a technology for all use cases. Now coming to the iris. Iris again is highly accurate, not as accurate as the retina, but again, it is accurate. It is a private part, even though people are able to see your iris from front, but they cannot see it to that extent. And health and hygiene is also high because you are not going to touch anything. You're not coming close contact with anything and it is secure, cost effective. Like I said, you can take a picture of your iris from your regular smartphone. It can be used on smartphones. And there is not much exclusion. Like you can do it even on babies. Babies also form there. The iris is already formed in babies, so you can use it. And I'll come up with a use case where it is already used, and especially in SSI and biometrics together. Ares of use could be a concern, but it's not that high as a retina. So some people, like the babies might not like it when you take a picture of their iris, but again, it's not that big a hassle. So this is a very good area for biometrics. The next one is palm hen. So palm hen is one of the latest area on biometrics. So the palm hen is an internal biometrics, which means that your biometric code is never exposed to the outside world. So you can take the picture of the internals. So this is secure and accuracy is very high over here. This is a private part. There is no issue in health and hygiene. There is no exclusion. You can do it on babies or elderlies. Everyone has got a unique palm hen structure. Ares of use is also very high. You are not going to touch anything. You just have to show your palm hen and the camera will take the picture. But it actually needs a new technology for taking the picture. So that new camera or new scanner would be needing some kind of investment and some learning curve is there. But then this area is picking up very high. So you can see the comparison of all these different biometrics types. And you can see in terms of the aids of use and accuracy, palm hen seems to be the best at the moment. But considering the fact that palm hen cannot be used with smartphone, the next possibility is Ares. And also you can see that people feel comfortable because when you think about a use case, you might not go with the best technology, but the technology which everyone can use. So hand geometries or palm hen is one very good area. But then I would say that Ares is the one that we can use immediately without further investment or without need to learn more. Now coming to the spoofing. So what is spoofing? So biometrics spoofing is a method of fooling a biometric identification management system where an artificial object like in this case of fingerprint mold can be used, a silicon fingerprint mold can be used and presented to the biometric scanner which leads to the hacker to get access to authorized data and services originally mean for the rightful owner. So spoofing can be done at various different stages. So like you can see in the right hand side, you can see this diagram where there is a sensor. And then there is an internal system where you are doing the extraction and all those things. So mostly the hacking takes place at the sensor level. And that is called the number one which is the presentation attack. So people come up with a 3D mask or a photo or a video or a makeup surgery or if they're doing it for a fingerprint and with molds. So the sensor is the area where maximum attacks happen. But then that could be attacked internally also but if it happens internally, then maybe they would get access to the data, the templates of course, not the original data but templates of a huge number of users. So that is an area that can be handled with the right use of SSI. But when we talk about the capturing the biometrics that would be always there. So we have to deal with the presentation attack and how we do it is through anti-spooping techniques. So when the presentation happens or maybe when the biometrics is captured, at that time you'd be given a challenge.  So all these things you have to do. Then there are sensors and dedicated hardwares which can check, we can do additional checks. So see the thing is the smile or facial expression you can do everything on a regular smartphone camera. But the sensors when we talk about sensor or dedicated hardware, those you cannot do on your mobile device. Those are for a different use case perhaps. So there are sensors who would ask like, sorry, if you are giving your fingerprint, then they might check what is the temperature of your fingers. So from that they can figure out whether it's a real finger or a mold. Similarly, there are algorithms which can check the high-regulation pictures and figure out that whether it's a real face or a fake one. So these are the different antispoofing technologies. So now, liveness detection tests. So these are called actually liveness detection tests which mostly people do in authentication purpose on your smartphones. However, liveness detection test is not needed in Palm Bain because in Palm Bain, the camera is actually taking a picture which is not starting, it's a dynamic picture and it is able to figure out whether the blood is even moving inside the blood vessels. So there is no need of doing a separate liveness detection test in Palm Bain. Now coming to SSI, I'll quickly browse through SSI. I believe that most of you might have good amount of idea on self-servient identity. So self-servient identity is a new type of digital identity where the issuer keeps on gathering verified credentials from different issuers. So the issuer is actually sending the verified credentials to the user's digital wallet and the user can share it with verifier. So here, let's say the issuer is somebody like the passport office and the verifier is somebody like the visa office. So while the issuer is sharing the data with the user, the issuer is also sending a reference hash to the blockchain and the verifier actually is able to know whether the data is valid or not, whether who is the issuer and the data is valid or not from that hash. The issuer is converting the data to hash and checking it themselves to know that whether the data is valid. So at a later point of time, if the issuer wants then they can revoke it by changing the hash on the blockchain and the verifier would be able to know that it has been updated. So there are so many different properties that we achieve through this kind of architecture, the integrity, ownership, privacy, security and validity of the data. So, but in a real world, what happens is like in a real world, we have multiple issuers. Like let's say the first issuer is the hospital where let's say Alice is born in a hospital. So the hospital is issuing the first set of certificate. Now the second certificate is provided by the government, maybe something like a national ID. The third one is the educational VC and the fourth one is the employment verified credential. And those data are collected on the users or the holders mobile device. And the user can share it too with the different verifiers and the data can be shared in different means like it can be a traditional sharing or general knowledge proven, selective disclosure type of sharing or it could be a self-attested sharing. And also there is a public ledger on which the deeds are decentralized. ID are created, reference are signed and revocation happens. So what is actually something that we keep on forgetting that the first issuer is the one who has the maximum responsibility over here because the other issuers are actually the verifiers also. Like let's say the first issuer is mostly the government. Let's say the government is providing you the national identity, which is unique and you are showcasing it to different verifiers and they become the second issuer to the third issuer like that. So it works like a chain. But the responsibility of the first issuer is maximum because they also have to do or deduplication of the data to figure out that whether it's, I mean, the user is not creating multiple fake identities for themselves. So deduplication is also an area that we have to not take care in an ideal world. So now we also have to, SSI is pretty complex. We have to take care of the web standards, authentication standards and the open source blockchain part and also the identity part. So there are different part of the entire ecosystem. So how it works is as a user, Alice is keeping all her data in her mobile device. Whereas Bob is keeping the data in the mobile device as a local storage as well as a copy on the cloud storage. But please note that the biometrics template is something that is not to be shared on any other device. So the key in this kind of architecture is you have to keep your biometrics data as much possible in your mobile device. Do not send it over, do not share it to any cloud storage. Do not share it, send it to any third party as much as possible. So the organization can always keep their encrypted data in a, data vault in a cloud storage. And also there is a public DLT. So which is just, I know, just making sure that all the deeds are created properly and the transaction has to start there. Now let's come to our third is next-gen authentication with decentralized biometrics. So here what we are doing, we are actually working with different kind of biometrics. Like we are working with fingerprints which many people are doing. We are working with iris and face as of now. We are also thinking of, we are just coordinating with other organizations where to make sure that how hand geometry can also be captured, whether it is possible or maybe for as a future use case, we are researching on that. And also we are researching on different behavioral biometrics because the voice is something even that a mobile can capture might not be a gate. Gate is the way you work, but voice is something that the mobile device can actually evaluate. So that is for future, but we are actually doing analysis on all this part. So how the entire thing works is, user first captures the biometric after lightness check and converts it to a template. So that happens in your mobile device and the template then goes to the issuer. The issuer, you can see a number, I have numbered them for you to understand. So the issuer is doing a background verification. In number three, you can see the background verification. Then the issuer is also doing de-duplication. So this is not needed for all issuers, but maybe the first issue or maybe depending on your use case, whether what kind of use case you have, do you need a de-duplication or do you not? And then there is a template storage if everything goes right. Please note that here, we are still using a centralized biometric template database, but that is only one time. The first time when the user is getting authenticated against the first issuer. And next time, number five, come to number five, when the user is trying to log in, at that time, it is the biometrics, the live biometrics is again collected and compared against the one which is stored on the mobile device. And then you can do it even one level of encrypting can also be done. And also then it is sent to the verify, the hash is sent to the verified and verify can check on the blockchain that whether the hash is something which is certified by the issuer. And if all goes well, then user is considered to be authenticated. So this is something, a very similar use case is done by Q-ledger. And you can find it in the news that they have gone to production with this use case. And so Q-ledger is basically a credit union in US and they are associated with multiple other banks and corporate societies, most probably. And once the user logs in to the credit union, Q-ledger, then the others, it's just like, perhaps like an SSO. So the other verifiers trust the credit union and they give access to the user. So it happens on Hyperledger Indy and I do not know whether they have started using biometrics. Maybe they are using fingerprint but I do not find that news anywhere. I don't see that whether they're using actively biometrics, even if they're using, whether it's fingerprint, I think it is fingerprint, I'm not very sure. But we are doing the same use case with multiple different biometrics templates. Depending upon what the user need, we can do different biometrics types. There are certain other use cases that I would like to discuss. First of all, I respond. So this is again a use case which you can find that this is a use case. I think this is either implemented in Africa or South America by a NGO called Irispond. And they're using Hyperledger Indy and this solution is, I believe, is on sovereign. And so basically, this is a special use case here. This use case is implemented in a country where the mobile penetration is low. So you cannot use a mobile. Maybe people do not have mobile or even if they have mobile, they are not smartphones. So obviously, the architecture cannot be something like what I discussed over here. So it has to be different. So how we can handle a use case where the user doesn't have a mobile device. So in this case, what happens in these countries, third world countries where a lot of kidnapping happens of the children. So people kidnap the children and take them across the border and then they're exploited. So what those governments are trying to do is they want to issue a digital certificate or decentralized digital certificate to each child and so the child can be a newly born baby or the child can be anybody who is less than a particular age, maybe 18 or 16 whatsoever. And there is power of agony or digital power of agony which is assigned to the child's legal guardian or custodian. So how that happens is the child's decentralized ID is created by an NGO and that decentralized ID is associated with the child's IDs. So it's not the fingerprint but they are using IDs. Why it's because fingerprint is not something that you can get correctly from a baby. For them, it's not well-developed. But IDs is well-developed. So they take the picture of the IDs and convert it to a template and then associate that with the decentralized identity of the child and the parents' biometrics as well as the deed are also created and they are linked with each other be it a parent or custodian, whoever it is. Now when the child is taken across the border, I mean to cross the border, then the border security officers have to check that whether the child is the right child and whether the guardian has given their consent. So how that would happen? Because these people do not have a mobile device. So a printed copy of their deed, a printed, in fact not a copy, it's a QR code of all the details are handed over to their parents by the NGO when the deed is created. Now when the child is trying to cross the border, at that time the border security officer would check that whether it's the same child and they would also ask for a digital consent by the parents, by the rightful parents and they would do it. And once all the things are validated, then only the child would be able to cross the border. So this use case, if I respond is actually, I think it's an UIT, I'm not sure whether it has gone to production, but it was in news lately. So yeah. The second one is airports of tomorrow. So this is not a use case that people have implemented so far, but I'm just, you know, I just got curious if this can happen and we are also exploring this use case. So this kind of technology to scan somebody's face and to scan somebody's iris in the airport is something which is already happening but in a centralized way. So there are automatic check gates in certain airports in US and the UK where from a distance, they would be able to scan the person's, from the person's gate or the person's face and then there is still some level of checking but then the user would be given access to go without doing so much of struggle. Like, you know, at today's state, you know, we have to do, if you go to something like JFK airport or London Heathrow, you have to wait at the kiosk, you have to wait in kiosk for the security check-in and boarding, but here a day would come in future where, especially if we are using SSI because even now we are using it in a centralized way. But if we use SSI along with biometrics, maybe the person's biometrics would be captured from a distance and the person would be allowed. So how that would happen is like, when the user is booking the ticket, from that point of time, the user's identity checked. So this is a use case where the user's identity checked at many different times. So when the user is booking the ticket, the first time the identity checked, then the user is entering the airport, the second time the identity checked. Third user is going to security check-in at that time also the identity checked and finally when the user is onboarding. So there are so many different checkpoints where the biometrics are checked, but then currently it is being done in a centralized way, but in future maybe we would combine the SSI along with biometrics and what kind of biometrics we can use because today we are using fares because it's relatively easy and many people are using it already, but tomorrow maybe we would be either using iris or we would be using the gate or the way the user is walking. So from a distance, they would be able to know that this person has already booked a ticket, this person has already done the security check-in. So let the person board the flight. So there are certain information that the user would share from his or her mobile device in the pocket and from approximately all the doors would be open to them. So there would be no more queues, there would be no more queues and there would be complete automation of check-in and boarding. So this is a future use case. So we are researching on this one as well. If possible we would be doing it with the gate or the way the user walks in. So what are the SSI success factors that you have figured out the first of all? So how seamlessly we are integrating the biometrics and what are the different kind of biometrics that we are integrating because like I said, different use cases might be needing different kind of biometrics. Then is your solution scalable? We have to always look into it if it is a scalable solution and how soon you are doing the entire thing like authentication, the people will not wait if you keep them waiting for a very long time. Also if your false rejection and false acceptance is high, that would not work for authentication. So depending upon that, you have to figure out whether these solutions are scalable and throughput cyber security. Of course you have to figure out whether cyber security is good enough. You have to do a lot of negative testing to check that your solution is working perfectly. Interoperable in a future date may be a different kind of decentralized identity networks can be completely interoperable and even if it doesn't matter whether you have created your date in one application, you can still go with other application and do something else and still it would work. How nice is your selective disclosure and zero knowledge proof work. So that is also a success factor of your application and also who are your validated nodes. So that also plays a major role. Like some people keep on telling me that who are the validator nodes. So are there some companies who are well known? So you have to see that what kind of validate, who are your validator nodes and this is one area that would lead to fetching more clients for your application for your services. So I think I'm done with my presentation. So if you have any questions you can ask me right now or maybe you can ask me on LinkedIn. I'm available. Yeah, looks like Nikki Hickman has raised a question in the chat. I can read it out. One is the one that talks about the deduplication. It says, can you explain a little more about deduplication? Is this deduplication of biometric templates themselves? In which case, why? We already know that many biometrics need to be refreshed or is it a deduplication versus individual unique identity? Is this case, in this case, how does this sit with the fundamentals of SSI that you can have many different quote-unquote identities or is it just data cleansing? Okay, yeah. So first of all, like I said, deduplication is not for every use case. It is for certain use case. Like I was showing you this slide where there are so many different issuers. It's only the responsibility of the first issuer. So mostly the first issuers are the government. Like if you go to the Singapore, they are creating a national identity based on SSI. So the first issuer's job is deduplication. So that the user does not create a lot of fake identities what people are doing as of now because we don't have that kind of system to check it out. We are not using biometrics in many places. Now the first issuer, so let's say the first issuer is the government. So the first issuer has to do background verification that whether the person is the right person, like let's say in Aadhaar in India. If you have to create your Aadhaar ID, then people even come to your home to check that where your address is the right address. So the background verification has to be done and deduplication also have to be done. So this deduplication is not only on your address but also on your biometrics data. So why it is so is because let's say I got five people in my home and all our addresses are the same like me, my husband, my kids and my parents also. So all of our addresses are the same. But then our biometrics are different. So that is why a biometrics matching has to be done to make sure that it is a unique identity. Now what she said is also right that at a future point of time, let's say your biometrics are changed. Let's say that your biometrics is based on your face. Now your face has changed drastically. Like even today, my children get a passport which is valid only for five years because they are up to a certain age. So depending upon that, maybe you need to change the biometrics. So that also is a separate use but for all these things you have to do de-duplication. Otherwise how do you know who the person is? If it is the same person then we have to update it. If it is a new person then also we have to check there is no duplicate in the same biometrics. So of course there is a de-duplication process which has to happen especially at the first issue who is like a government. So in the right hand side I am showing that this feature can work for the authentication of the same issuer or maybe for a different issuer just like what the Q ledger is doing. So you can log into the same system or to a different system. Just like in ADAR, you can log into ADAR using your same data or there are certain banks who when you create accounts with them, they actually log into the other database to check that whether your biometrics are matching with the person whom you claim to be. So obviously there would be a need of de-duplication especially when the security is very high. Okay so her next question obviously in that stream is that the fundamentals of SSI says that you can have many different quote-unquote identities which obviously are the pairwise dids or something like that but underneath all that is the same person. So there's a contradiction there she's or he, I don't know whether Niki is Hi sorry to let you it seems like it's very hard on that. Yeah the I'm interested in this de-duplication as you explained it it seems to always relate back to this initial issuer this which you suggest is normally a national government or a government ID and I just wonder around continually referencing back to that whether that might not build centralization and control points which which is obviously important for some use cases within a particular ecosystem but doesn't necessarily give the full range of benefits I'd say to the holder the SSI promises does that make sense actually hi Niki yeah you are absolutely right there are certain use cases which needs a person to have multiple identities like let's say that you have five different devices and those devices are all linked to you and again those devices need to interact with each other what is what's going to happen in IOT but that's a different use case so that has to be handled differently but I'm more inclined towards how a national identity works so in national identity your biometrics have to be kept somewhere so as of now we do not have a technology where we can completely decentralize it but maybe in future we can do it but as of now because even if you need to compare your biometrics with some reference what would be that difference and if we say that we have to we don't want to keep a decentralized biometrics template we are actually working in this area we are researching a little bit in this area if this can be decentralized but as of now it would lead to security issues because we have to compare the biometrics template with an existing one so yeah this is an area of research I would say and what about go ahead Niki do you have anything more yeah I mean personally I just see a biometric as another attribute and in that way you could see a template as just another form of credential and so it kind of concerns me that you're still storing this data it should be maybe it's a dream but it should be sufficient and the biometric is just used to bind a living human being to a particular credential or set of credentials and therefore the proof that the verifier has just needs to be the link between the person in front of them or the person they're transacting with and the proof that they've got from the chain in terms of the verifiability of it I can see that within a national system they want to always go back to one unique individual but I think that's missing lots of opportunities yeah it's a big thing in further risk like you could embed you know in this model if you can hack the initial issuer then and kind of force an update and yeah actually the way biometrics comparison is done is very different from the way you compare the alphanumerics so in alphanumerics it's always in the comparison if it matches then it's a 100% match in biometrics it does not always match 100% sometimes you can have false rejection rates also even if you are the right full owner so why that happens is because it is actually giving rise to a biometric template which is 101010 like that so let's say that you are pressing your thumb a little differently let's say that your thumb is a little it has got an injury I mean there could be very small differences and depending upon that it can lead to rejections it can be close match so the way biometrics is compared is very different from the way alphanumerics are compared so that is why what we are doing we are still keeping our centralized repository like I said in future maybe we won't need it but I mean we can come up with the technology where we won't need it but as of now we have to keep a centralized repository especially for the biometrics the first issue which is like a government is keeping our centralized repository and the part that we are sending as a hash this is the first time hash and further you can see when the user is logging in again and again and again again we are not going back to the issue we are just checking that whether the hash is matching with the version which is there on the same mobile device this is the first time we are doing it but like you said I am also looking out for a solution where we won't be needing this centralized biometric template anymore but how soon we can do that only time will tell because I live in a country where we have Aadha which is the biggest biometrics database in the world so that is why the concern is over here everything is interesting research look forward to the results of that so if it were not stored in a centralized database maybe it would be but let's see why it is stored in a centralized database because you are going through this process of deduplication and all that and if you lose your device you lose everything then you can recover it that is one of the things otherwise you have to go through the same process again right the second thing is if the match which I believe is a fuzzy match we actually have a paper a working group that was donated by IBM which is one of our first papers which talks about how this whole process works and we have it linked in our page it's about the way the template is calculated and also about the matching algorithm itself because it's an exact match and it's a fuzzy fuzzy match and the point is maybe there is a way to create that storage in an IPFS or something like that that is much more decentralized but I have seen some models where instead of the issuer having the centralized biometrics or looking after that aspect of authentication they have a biometric service provider that would work alongside your agent or wallet provider so that's decentralization at one level it's certainly not relying on a single issuer to be the kind of owner but then you're centralizing the storage somewhere else I mean even though it is a service provider they can be hacked as well but that's kind of the choice of the individual as opposed to the choice of the issuer and you could have multiple biometric service providers just as you could have multiple agents and wallets yeah I mean in the end if you accept that it has to be stored away from the device or from the address then you have this problem anyway it has been a delightful hour we are at 1001 and many thanks to Debiani for this wonderful presentation and to you Niki for the questions one of the things I want to emphasize here is that the identity working group is looking for presentations and we want to be inclusive and diverse and I have deliberately chosen the first presentation this year to be a woman because that is very important we have very few people presenting that are truly diverse people and I think that is extremely important and I also run this other group called the Hypology Capital Market SIG and I have chosen that so 2021 for me should be where we showcase the diversity of our membership in our community so please get back to me if you are interested in presenting or have any ideas about how to go forward here and many thanks to Debiani again I hope she will share her slides with us I will do that now and then make the slides and the recording available on the site that I posted on the chat and I can probably reflect it back in LinkedIn or some other space as well so that people can find it thank you again and our next presentation is going to be two weeks from now thank you


Rwy'n cael ei wneud y cwestiynau ar gyfer y dyfodol. Rwy'n cael ei wneud. Rwy'n cael ei wneud ar Andrew a Friedrich. Andrew, wrth gwrs, rwy'n cael ei wneud y cwestiynau yng Nghymru, ydych chi'n cyfrifiadau cyfrifiadau ar gyfer ydych chi'n cyfrifiadau.  A Gweddorach, rydyn ni'n ddigon ni'n gweithio ar y cyflwyngau. Rydyn ni'n sefydlu bod chi'n gwybodóa chyflwngor o ran ddweud yn hollwch gweithio, yn rhan oherwydd mae'n gweithio bod chi'n gweithio'n gweithio'n gweithio'n gweithio. Ychydig o bwysig o'r cyflwyngau ar y gweithio'n gweithio ar y cyflwyngau oherwydd eich ddweud yma oherwydd i ddweud. I'm not sure if you've controlled for that or not, but a more general question here is about the sort of selectivity worry by choosing to look at couples and people who are currently couples. Now, the people who are currently couples might have become couples a week ago or might have been couples for years. We don't know that, but they're couples and of course there's other people out there in the population who are not, who may be quite different and because they're quite different may be exactly why they're not couples. So, I just wondered if there's a possibility to compare the couples to those that are not couples, as well as, as I said, looking at how long they've been together. Thank you. My question is for Mikael and I want to talk about the negative sign that you see on increasing taxes that you find after your treatment. And I'm concerned if this could be because people do not really view taxes as an effective measure of, as an effective means of redistribution. So maybe if you had something more direct, like say you asked people that say we increased taxes, but at the same time we had some program that gave a transfer to people, then maybe you would see a more positive impact there. I have a question for Frederick and then Andrew. Frederick, in the bargaining literature, it has been found that the partner age difference has an impact and in your case it's not significant, but I was wondering whether looking at the houses headed by males would have an impact on your disease. If we had included the houses headed by females, would you find something different? And then to Andrew, there was, I think on the proportions of a non-race in 2007, 3% in 2013, 5%. And I'm wondering whether that there is an increasing trend of, and because in some cases I found something like 74, is it an issue where people prefer not to reveal their race. And secondly, on your CDCs, the cumulative density functions, you only looked at black and white. Wouldn't you extend, and if you did, what are the findings? Because I noticed that it flattens out towards the higher levels of the capital income. So I have a question for Frederick. So I guess the one thing which worries me slightly is that your number of observations after you filter it is right down to 344. And the particular problem I have is that your test statistic is an LR test statistic. And we know that basically on this complex survey data, which NIDS is, that actually likelihood-racious statistics are really not appropriate because I assume that you've got independent sampling of all of these things, which with the clustered sampling really is a problem. So once you kind of put a cluster correction in there, it kind of worries me what you've got left kind of like with that sort of sample size and whether you kind of thought about that a little bit. Hi. My first question is for Frederick. I was just wondering about your husband's language, husband's bargaining power variable. Could you explain the, so husband's mother's education as a share of all mother's education? And the second question is for Miguel. I don't know if I missed it, but did you show us crosstabs for treatment versus control characteristics? I don't know if I missed it, but. OK, this is about a Friedrich presentation. And I have a couple of very technical points. I will not mention them. I would like to support the point made before about the fact that some of those characteristics that you call preference parameters as a matter of fact may also affect the bargaining power, the Pareto weights. And certainly the time that the people have been living together is quite important. The number of children. I mean, if you have two children, three children, presumably the bargaining power must be different. I mean, the equilibrium within the household must be different. So it's not clear the way in which you can handle that because you'd like to make a distinction between parameters which do affect the bargaining power without preferences, but certainly it is something that has to be taken into account. The other thing which I found a little disappointing at the end, we are talking about inequality. So how much inequality do you have in those households? So we know that you cannot identify the sharing rule completely because there is a constant that is missing. But at least you could do some kind of simulation analysis. It's OK, assuming that there is a kind of normalization of the data.  What is the kind of inequality that we're generating? Is it OK that as in the paper that you mentioned at the beginning, these insights, you are getting 50% of the total inequality is due to inter-housel inequality in South Africa. I think it's very important to make these. And my second question is about it to Miquel. It's a little related to the issue of what about the control. But because this information on international inequality is important, could we know how many people in the population initially knew something about inequality in Namibia as compared to inequality in South Africa or as compared to inequality in the US? Because there is always an issue with this kind of experiment on information, which is that there must be already some information in the population. And the problem may be more on the way in which the information is spreading among people. So it seems to me that it would be important to have some complementary information about these. So we'll start with Andrew and then go in the same. Thanks for the questions. Yes, there are differences in the types of programs that people apply to. I haven't investigated that systematically. But I do know, for example, that the differences between black and white students in the commerce faculty are much smaller than they are in the science faculty. Which would suggest that better quality black applicants are applying to commerce rather than to science. So I can definitely take that a little bit, do a little bit more thinking about that. So thanks for that suggestion and comment. Does the process actually work differently? The university process, for the most part, no, it doesn't. Different faculties and programs have different requirements, but they all advertise those requirements and they're not really taking stuff into account other than basically your secondary education results. And in some cases an extra test that's now come about the national benchmark test because the universities are a bit worried that maybe the secondary education is not quite a good predictor of how people will do in universities. So there's this kind of university generated test that's now also being used. In medicine, for example, they used to ask for CVs and references and things like that, but I think in the data that we're using, that's not the case for these years. Trends in not applicable? Yes, I think that's an important issue. So many, I think, are white students who think that if they say not applicable, they're going to somehow be treated better than if they say that they're white. That's not the case. The university puts everyone who says I don't want to say or whatever into the open group along with the rest of the white students. And so I would agree, yes, that probably there is some sort of sense. I went and actually had a look at the data and there was definitely some white, what looked like white candidates from the very sort of higher end of the schooling. So they went to that elite private schools look like they're probably white students. But so there is that trend. And in the CDFs now, I haven't drawn it for the other groups, but that's a helpful suggestion. Thanks. I'm going to answer the question in the order in which it would also. In terms of the relationship length, the selection. So I don't think we observe relationship lengths in the net data, but obviously this is an important factor. If it was observed, we would have included it as a control variable. And then a related question was just the generalisation. So the idea was to restrict the sample to the persons we have as I don't really think that just putting in a dummy variable for just for certain aspects, just for a four person household. Or adult member household, for example, is sufficient to account for the different level of bargaining power that we would observe. We would need to do a far more complex econometric analysis. And at this stage, I'm not exactly sure how we would do that. And then in terms of the age differences and the male-headed household's heads, this is also somewhat related to Professor Benion's question. So we wanted to separate effects that we expect only work through the sharing rule on household demands from effects that we expect maybe also due to preferences. We do not have a price variation in the same way so that when we see changes in, for example, the age difference, we don't know if it's maybe just because the older household members in general prefer higher expenditure on medical, for example. And that's not exactly clear. And similarly for male-headed households, there may have been some pre, well, some marriage market interactions or something like that that happened in female-headed households. So a household that identifies female-headed households that are not necessarily able to control, but if I recall correctly, our results weren't that sensitive to it. We just didn't want to do it. In terms of the number of observations in LRTC, that is a very, very valid remark. Rulof and I are redoing the data on the upgraded NETSET to Wave 3. We're attempting to incorporate far more observations. And we're also looking to a few more methods to test NETSET and NESB because, yes, this is a very big issue. Currently we're looking at a sample that's quite a bit more, but it's getting there. In terms of the husband-husband maternal education share interpretation, the idea is in South Africa because this data set is a 2008 data set and our sample is of people that are between the ages of 25 and 65. The idea is that the husband's maternal education share gives an indication of potential wealth or access of the partner's pre-marriage. So we would expect that if one spouse's mother was better educated than the other spouses, we would expect that person to have better access to education opportunities and eventually labour market opportunities or income. The bargaining power difference. We do not attempt to identify the sharing rule in the paper presented. We were, once again, mainly focused on factors that we do know are not then correlated to other preference factors. But, yes, this is a valid critique and I'm not exactly sure how we would control for this other than perhaps interacting those preference factors with the distribution factors inside that lambda equation. This is definitely something to look at in terms of how much inequality. So, yes, maybe I've gone through that a bit far. So what we see is that female bargaining power, at least as is defined, is far, is on average way less than male bargaining power in the households. But it's definitely worthwhile for us to go further and look at maybe a better accurate measure of inequality inside the household when we actually observe these things. Because we only observe effects of changes in the sharing rule and not the sharing rule itself. Thank you. OK, so first about this balance test, like whether for the controls there's different between the treatment and control stuff. So, yes, I didn't show it. So the survey was done with mobile phones and so the randomization was sort of inbuilt in the thing. So I mean it should be fine. It's fine. There's like I think for some treatments like some people are older but in ways that are sort of stuff where even if the randomization works like sometimes things are imbalanced. And with all the results we put a bunch of controls of demographic and pretreatment stuff and everything is the same. So there's no problem with that. The issue of whether people identify this taxation with redistribution. So actually we do have a question on I think general taxation and to spend it on, I don't know, on stuff. And from what I recall actually there's not much action there. My feeling is that so the one that we have here about the top tax we were careful to emphasize the sort of that we are talking about the rich taxes for the rich. And that we were saying something like, so now I don't want to get the numbers wrong. I don't recall what were the numbers, but something like 60,000 grants per month. A person earning 60,000 times per month has to pay say on average 20,000 in taxes and keeps 40. You think that person should pay more of it. So I feel that this one is the one that maybe is like most clearly linked to emphasizing someone that earns 60,000, which for the incomes of the people we are talking about is something massive. Whereas the other one might be that is interpreted in a rest actually less redistributed way, just like okay you tax a bit more and then you offer more public services. And the last question I have to say I didn't understand it exactly. So we do have information on what they thought inequality in South Africa was. So we could check if the proportion of people that said something lower than what we actually showed them. Is that what you are saying? The knowledge of inequality in other countries in this chart that you have shown, how many people knew that there were countries where inequality was much lower than in South Africa? So I guess that the best would be that. So obviously we don't know. We cannot know what they knew about the others, but then I guess that by checking what they thought about South Africa relative to the others like we can see. I guess the point here is to make a distinction between knowledge and sensitivity by showing those chart to people. Maybe they had some knowledge about the fact that South Africa was definitely much more unequal than other countries. But simply because you are showing this, this is triggering some reaction from them. So it is not an issue of knowledge of information. It is something else. This is the point I want to make. So I mean in some sense, yes we wanted to be something else. We wanted to be something that makes people like shocks this inevitability thing. So we wouldn't now advocate a policy of like now we have to distribute information and that's what will work. No, we wanted to explicitly shock the perceptions of people and whether this happens to the information channel or through the sensibility. We would have to think about whether it matters a lot. Right, so we have time for a few more questions. Thank you. So I have a question for Laura. You are showing that the poorer under-report ill health, but then your conclusions are all about, so you say then that the inequality as measured in self-reported health is underestimated and that's true. But what are the implications of that? It seems to me that turns a lot on why the poor under-report ill health. Like is it you implying at points that it's endogenous that because they can't afford to be ill they under-report ill health. But it could just be information, right? It could be the links between education and knowing how healthy you actually are and it seems you somehow need to benchmark, I don't know if you can in the data. You mentioned that you've got sort of objective measures of health and then you've got subjective measures of health. And it seems to me that the relationship between those two is very, very important before one extrapolates to saying that this has got huge policy implications for the delivery of health services rather than just the understanding of people about how healthy they are. OK, and then I had a point for Fredw. I think some of the comments have been around the fact that you clearly reduced your sample and you tried to put it into a nice structure to help you make tractable what's already quite hard to make tractable by these couples. And I think a lot of the comments are about that the reality of life in South Africa and South African families and households aren't like that. So you might not be making things tractable and the trade-offs in terms of the sample size might just be too hard to bear. So I'm not sure what you lose by opening it up to a more complicated version of the household, a South African type household. That's the question. Then I wanted to draw your attention then to the questions in the NED's data on who makes the decisions about this and that, the next thing. The so-called bargaining questions, whether those can't be useful to you somehow in these models and in separating out these different effects. So there is this paper on the South African pensions because there was a lot of speculation in a similar type of literature to you where people were just using cross-sectional data but talking about unitary versus bargaining models of the household and doing exactly what you're doing. And there's this paper from Kate Ambler out of the University of Michigan that uses those questions about who's making the decisions to try and come into that very same issue more tractably. So that might be useful to you as well. Yes, a small point to being there. I don't understand why you look at the average reaction to the politician speech. I would like to see what is the distribution of the reaction as I would expect. I don't know much about the political situation in South Africa, but I expect opposite reactions from people sharing or not sharing the view of the politician who is talking. So maybe a slide with the distribution of the change would have been interesting. Maybe you have a kind of polarization of the reaction and this negative sign you find is simply like a result of reaction in opposite directions. All right, so then you can answer questions. So I think that is a valid question about the transmission mechanism and I do a lot of theorising about it, but it's tough to control for it and even if I do control for objective health measures, I'm not sure if that would tell me anything about the actual transmission mechanism. And also the stage data is very much self-reported except for symptoms. One possibility if I did want to do something looking at objective versus subjective health is to look at the next data set, which has more objective health measures. But also I'm not sure if it really matters what the transmission mechanism is, if it's a lack of information or if it's suppressing of health, of the idea of your health, either way the health needs are going unmet and that you should have better access to meet these health needs. Okay, so on the reduced sample and what do we lose? So yeah, so when I initially started this research question, I was also far more interested in the households where we have six working age adults with children from each household. And I think the main issue for me at least conceptually is that we would expect the distribution factors to affect these persons differently depending on their relationship. So we would expect the balance of power between a married couple changing those distribution factors for those to be different than those for the brother that also lives in the household with his own children. And I'm not exactly sure yet how we would apply this, but yes, this is most certainly the case. And then the bargaining question, so the reason that we didn't use the bargaining questions as it is presented is it's one, it's ordinal, well it's ordered so it doesn't give us the same sensitivity. The other thing is like there might be a difference between what the household members that is currently answering the questions, the person thinks he is the boss or she is the boss, where this might not necessarily be the case. But we do use that data later on in a probate estimate to see whether or not the distribution factors are indeed significant in predicting the results. And we see that yes, they are in the same direction predictive of those results. Is that all the questions? So about looking at the distribution of reactions, so I guess we have to think about what indeed what I had thought about and what is going in the direction of what you said that I think would be really nice is to try to identify groups that might have been positively shocked. And so the way actually in some sense what I thought and what I was expecting is that the difference in the townships would give us this thing or differences of the videos or the way that the fact that the result seems to be so robust across different instances makes it hard to find variables. I was thinking more in terms of pretreatment variables, to group like the make interaction effects on the basis of pretreatment variables and maybe we can look at your thing. I mean it's hard because like for example in the Caillicia sample an overwhelming majority says that the ANC is their closest, so they support the ANC or at least they say that the ANC is the closest thing. So that would have been the people that you would have in principle say that should be sort of more convinced by this type of thing. And there are very few people that report like in Malema or supporting the party of Malema. So we will think about that. Any more questions? All right then, thank you everyone for being here early in the morning and thank you to the four presenters for four fascinating talks. Thank you.