 OK, thank you. So this is joint work with Debraj Ray, and it's sort of based on our past current work. So it's really motivated by the missing women phenomenon, which was coined by Amartya Sen. He essentially defined it back in the 90s. And it's really motivated. I'm sure everyone's familiar with this. But it was motivated by the fact that the sex ratio, so just looking at males to females in developed countries is typically less than one. If you go into developing countries, you see it the opposite. And in particular, India and China were focused on. And essentially Sen's big contribution was to quantify a measure of missing women. So effectively just want to compute the number of women who would have been alive if instead they had been born in the developed country situation. So essentially we think of what it was meant to be was the developed countries embodied where we think if there's at least gender discrimination, that's the sex ratio we should see. So that's where men and women received similar currently. It's not perfect, but it was the best that we had. And this resulted in a massive estimate of at his time 100 million, but these have now been updated to about 200 million. And so this is meant to be a measure of the women that are missing due to inequality and neglect. And to explain this global missing phenomenon, global missing women phenomenon, people have focused exclusively on Asia, essentially. And also on the sex ratio at birth. They really focused in on sex, selective abortion, female infanticide is a key explanation for this 200 million missing girls. So what Debraj and I did in our earlier paper, we said, OK, well, let's see. Are they really all at birth? Because Sen's relying on overall sex ratios, then the arguments have been around about sex ratio at birth. So is it the case? So to do that, we moved away from overall sex ratios. And we asked the question, at what age are they missing? And then we went further and said, what are the diseases that are causing this? And what we found about by doing this, we changed the methodology a little bit that Sen had used. But essentially, it's very much in the spirit of his work, was that in fact, the majority are older. They're older than 15 years old. So it can't be, it's true that the sex ratio of birth is biased, and there's sex selection abortion going on in these areas, but it's not explaining the 200 million missing women. So that was the first big finding that the majority are actually older than 15. And secondly, a completely overlooked issue was that a huge chunk of them are actually found in Africa, which was completely overlooked. And in relative to the actual female population numbers there, it's actually very large. So at least 30% of the missing women were found in Africa. So that's where we left that work. So what we're doing in this paper for this chapter is to dig deeper into the African missing women. So we're going to use the same methodology and find out how they're distributed across the country and what diseases are responsible. So just to show you what methodology we're using, so this was Sen's methodology. So he simply took the sex ratio in our country of interest to say India or China, the sex ratio in developed countries, minus one times the population of women in the country of interest. So then ours was we just want to move away from those overall sex ratios. But we're going to stick to the same counterfactual that developed countries are the reference country. So to move away from sex ratios, we moved into using date on mortality rates by age. So we're supposing for each age category that the relative death rates for females to males is free of bias in the developed country. And then we're going to compare this to the actual death rates, so it's exactly what Sen did. But I'll tell you exactly the formula. It is to motivate why we did this in the first place. So this is what sex ratios by age look like. So in the developed country, so everywhere in the world, there are more boys than girls born. So the sex ratio of birth is always biased towards males. And then essentially, the mortality of females is lower than males at all ages. So essentially, you get this downward-slopey relationship so that the overall sex ratio is less than one. And then if you look at China, it follows the same pattern where you see this huge jump at birth. So this is that birth up there. But then it's following the same pattern. So that's consistent with the story that it's mainly a birth, and then it looks similar to the developed countries. There's a little bit of a hump over here. But otherwise, that would be the standard story. But then what we were surprised by was when we looked at the sex ratio across the ages for India, it starts out high. At birth, it's all just way flatter, right? So this implies that just looking at this, that there are missing women all the different ages. And then when we put sub-Saharan Africa in there, the sub-Saharan Africa has a notoriously low sex ratio of birth, so they actually have lower than the developed countries. And that's partly because African ethnicities are more likely to have girls than anyone else. And that's been true in the US. And that's sort of an established fact. So that's sort of partly why looking at the overall sex ratios made Africa look good. But essentially, what you see is it follows a very similar pattern to India. So everyone was grouping India and China together. But when you actually sub-Saharan African India, you look much more similar. So this is what motivated us to do this. If you looked at the relative death rates, you see it difference more strongly. So this is relative male to female death rates by age. So this is the developed region. So you see that there are a lot of excess male mortality here, and then at the older ages too. And then you put in China, again, following a similar relationship, much more bias at birth. And then you go to India, or sub-Saharan Africa, you see the absolute opposite. So tons of excess female mortality. And then again, India following a similar pattern. So there was these two groupings that made more sense rather than the China India one. OK, so then we just computed these. So you take age. So at any given age, if A is age, A equals 0 is birth. You take the death rates of males as DMA, death rate of females as DWA in our area of interest. So what we assume is what the unbiased death rate should look like in our country of interest. So say India. It should be the death rate of males India. So this takes care of the mortality rates of different across India in the developed country, right? Divided by the relative ratio of male death rates to female death rates in our reference country. So if in developed countries the death rate of men and women is equal, right? So it's a one-to-one ratio, then the unbiased death rate of women in India should just be the male death rate in India. But it essentially sends exactly in the spirit of sin. And then you just do it by age groups. You've got what it actually is, what the actual death rate of women in, say, India is, whether it should be, times their population. And then you just sum those up to get the total missing women. And again, we're doing it because we're doing mortality rates. We do it just within a given year. So sin was competing the entire missing women ever, right? So we're just doing it within a given year. So this is from our previous paper. So we found 1.7 million in total in a given year in India, 1.7 in China, and then 1.5 in Africa. So in terms of population numbers, it's actually highest relative to female population in Africa. And again, there's none at birth in Africa, but then you see them at all the older ages. So now what we're going to do is look at this and see how it changes across Africa. So we're relying on data that has been a little bit criticize. So it's essentially the best data we have available. Let's put this for the global burden of disease study, which is a collaborative effort between the WHO World Bank and the Harvard School of Public Health. And essentially, it's the only existing comprehensive data set where you can compare across countries by age, by gender mortality rates, by disease. And it estimates all of the, sorry, it reflects all of the information available to the WHO. So any census data, any health surveillance, sites data, and so forth, sites data. And we're going to rely on the most recent estimates for Africa, which is from 2011. But you have to, there is a necessary caveat in that vital statistics are very poor in all developing countries, particularly in Africa. So essentially, they're making a lot of assumptions. They use a lot of epidemiological models. They use, make use of something like 200,000, sorry, 2000 model life tables to get all these estimates for Africa and where there's missing data. But really, it's the best we have. So I mean, the worry would be is there's some systematic bias this drive in our results. I don't think that's necessarily the case, but anyway. And the World Development Report in 2012 sort of replicated our findings from our 2010 paper using different methods and data and they found very similar results. So in that sense, these estimates are robust to serve a few different assumptions with regards to methods for computing these mortality rates. So anyway, you have to use caution, but it is the best we have access to. Okay, so this is now computing. By, this focus on two different age groups is zero to 14 and 15 to 59, which is where all the mortality in Africa is because mortality rates are so high so people aren't living much past 59. So this is now the actual number of excess female deaths in the different regions of Africa. And this is as a proportion of the female population. Okay, so you don't want, you will have large numbers just if it's a very highly populated area. So what you see is West Africa is where the most excess female mortality is for both of the age groups. Okay, so this is, so first 1.7 million per year. So in 2011 there were 1.7 million missing women in Africa. And then this is 200,000 in West Africa, the age group zero to 14 and so forth. And then as a percentage of the population, you also see it was big in Central Africa for the younger age group and then very high in Southern African countries for the older age group. So then we want, so that's the overall looks, then we want to see what diseases are responsible. So essentially the WHO divides the cause or death into three categories. You have first group one disease is communicomaternal, perinatal and nutritional and then two non-communicable diseases. And essentially in developing countries everyone dies of group one and as you become more developed you start dying of group two. And this is essentially known as the epidemiological transition. Okay, essentially in Africa everyone's dying from group one mainly. Okay, so this is just to give you a sense. So this is the age zero to 14, the different areas and the mortality rates. Mortality rates are highest. So you saw the missing women for highest in West Africa, right? But overall mortality is actually highest in Central Africa. And then this is what they're mainly dying from. This is the children, zero to 14, the diarrhea related diseases, malaria is a big killer, respiratory infection and then perinatal conditions are the big killers. And essentially where there's high death rates for this younger age, there's high death rates in these key diseases. And then for the older age group it's essentially HIV and Southern Africa is of course hugely, has the highest death rates from this. Okay, so that was what the overall death rates look like. This is where the missing women are allocated by disease. So this is the younger age group. So essentially, again, West Africa was the highest, right? And essentially they're distributed across these main killers. The diarrheal diseases, the malaria, respiratory and perinatal, but perinatal is only having a role in West Africa and the single biggest killer is malaria, okay? Okay, so this is what it looks like. So this is the zero to 14 and then this is the older age group. So 15 to 59 and you see how big a role HIV has, okay? So everywhere where there's, the single two killers are HIV and maternal mortality, okay? And of course it's very big relative population over Southern Africa. Okay, so then this is how they're distributed by disease and then we went to do it by country to see if certain countries within these regions were more responsible, okay? So this is now looking at East Africa and what you see is there's massive variations. So first it was interesting there's so much variation across the regions of Africa and then secondly, now when you go within these regions there's massive variation across the countries, okay? So for example, for the, this now tells you the missing women is zero to 14 and this is a proportion of the female population. This is 15 to 59. You can see there's just a few countries, right? So Ethiopia, Mozambique, Somalia and Tanzania are mainly where the missing younger girls are and then for the older girls it's again, sorry, the older women it's Ethiopia and then Kenya and then Mozambique and Zimbabwe and it reflects, and these larger numbers over here essentially are also reflecting larger percentages of the female population. Okay, so that's East Africa, this is West Africa where the most of them are and you see Nigeria as a big player in this but they're partly in the hugely populated country, right? So when you look at percentages it could, you know, Niger is as high and so is Burkina Faso as a female population but then as the older age group Nigeria is the biggest but also the biggest percentage, okay? And then other one is Cote d'Ivoire as a percentage of the population. Now this is North Africa, so North Africa has very few missing women in general. So Southern Africa has almost no missing women at the younger ages but tons at the older ages from HIV and North Africa has none. I don't know if this is a surprise to anybody but and if they have any at all it's in the Sudan, okay? Which would be what you might expect. And then this is now Southern Africa and of course it's South Africa, so almost none in the younger category and in the older category it's essentially, you know, the HIV, Southern Africa but then similarly, you know, high percentage, you know, a lot in South Africa but then as a percentage of the female population in the three other countries, Swaziland, sorry, Hanlisty too. And then Central Africa, we see that DRC very high for the lower age group and then as a percentage also Chad in the Central African Republic and then again DRC and is responsible. But again, you see these big variations. Okay, and this is just to show you what the distribution looks like geographically. So this is the excess female mortality as a percentage of the female population for zero to 14. So as we're getting to darker regions, it's more excess female mortality. So this is the age group zero to 14 and then this is the age group 15 to 59. So what's curious is that there's just so much variation and also it's not always the case that the older group, you know, if there's excess female mortality in the younger groups, it's also in the older groups, those aren't consistent and then top of that specific countries are playing a bigger role. So then just one concern is, I mean, this criticism of all this literature is using developed countries as a benchmark. So people think, well, there's so many other things that determine, again, it's just an approximate of where we think, you know, the least gender discrimination is and is the best we have, but there are other possibilities. So for example, we did, Dharajna did this work for India and we used Kerala as a benchmark. And in this case, what we could use is Southern Africa for the lower age group. Okay, so there we had very little excess female mortality. So instead, we recompute these numbers using the Southern African region as the benchmark. Okay, and in fact, we can only do this through zero to 14 because they have a lot of excess female mortality from HIV in older groups. But essentially, what this does is it actually increases our estimates. Okay, so, and it increases our estimates, particularly from dying from diarrhea disease, diarrhea disease related diseases and perinatal conditions. Okay, so what this implies is that, so on average, the overall estimates for the age group zero to 14 increased by about 25%. So essentially relative to developed country references, the relative death rates of males is even higher in the countries in Southern Africa. So what that implies, I'm not sure, but it's also relevant because partly when we're doing this disease estimates, we don't have a lot of deaths from say, some of these diseases that you die from Africa in the developed country. So we're careful with that. We don't, we essentially assume a one to one if we don't have enough observations. Then the Southern African data, we have way more observations. So for example, for malaria deaths and perinatal deaths and diarrhea related deaths, so these are, if anything, the better, more reliable data generating these estimates and they actually show us higher estimates. So what I conclude from this is like, if anything, the earlier estimates are lower bound on possibly what's there. They're not overestimates. There's no, there's no suggestion that they're overestimates. Okay, so then, so that's, so this is really, so that's the purpose of the paper is sort of showing you where they are, how this, you know, the variation, just providing a decomposition of these missing this excess female mortality in Africa. And we can't pinpoint why, okay? So that's sort of for future research. So essentially, but just for, it just, you know, really crude things and what are the possible mechanisms? And one thing that's just, this is just a graph of GDP per capita and these measures of excess female mortality. And essentially, there's no correlation. Okay, so it's not a simple poverty story. And then instead, if you plot adult overall mortality and this excess female mortality, you see a strong positive correlation, okay? So that is certainly, you know, higher mortality regions, there's more excess female mortality. So that's certainly true. But what becomes clear for these estimates is that it's not just overall mortality, certain diseases are playing a role, okay? So for example, excess, and it's not clear why, okay? So for example, excess mortality amongst young girls aged 0 to 14, well highest in Central West Africa, and these regions are played by high comparable death rate, overall death rates from diarrhea related diseases, malaria, respiratory infection, and perineal conditions. So essentially the overall death rate was very close amongst all these four causes. But it was malaria that caused the most excess female mortality, and then secondly, respiratory infection. Okay, so certain diseases have more of a role in explaining this. And then moreover, a certain disease can have a different effect by region, okay? So for example, overall mortality rates from diarrhea related diseases are significantly higher in Central Africa compared to Eastern Africa, but the number of excess female deaths from both of these areas is comparable, okay? So again, the diseases have a different impact in different areas. Similarly, you saw this in the older age group, I mean, I didn't highlight this, but tuberculosis had a bigger impact in Central Africa, but respiratory diseases had a lower compared to other areas. And then it does sort of, I mean, so again, we can't identify the exact channels for this. Likely it's a host of factors, right? So likely, biological, social, environmental, behavior, economic, which explain this variation. But just to put forth some points, I mean, so we just don't know. There's just no research on this stuff. So studying the gender differences by disease is a very new area of research, even in developed countries, right? It's a very, we know very little about it. So for example, like just take malaria as a big player, so why? You know, so for one thing, malaria control is threatened by the rapid development and spread of anti-malaria drug resistance. Possibly there's a gender component to this resistance. Yeah, we just don't know. This is never studied. For example, the acute respiratory infections, there is work in developed countries showing that there's a male bias to this. And there always has been. Your male is more likely to die from TB before and so forth. Historically, I think that's true. But then we're seeing the opposite. So if these estimates are accurate, it's higher in Africa. So there's a female bias to this instead. So there's possibly a lower relative protective immunity in Africa for females. We don't know. But anyway, there's likely a biological, some biological aspects to this. And then of course, and then there's HIV. So this is a massive killer. So 800,000 missing women from HIV a year in Africa. And it's the only place in the world where women are more likely to die from men of HIV. And it's extreme, right? So it's on average for all age groups. 1.2, if you go to younger age groups, it's even higher. And this is relative to any developed countries, any other developing region in the world. Men are way more likely to die from HIV. This is a big study topic. And essentially, there are biological differences for every given heterosexual interaction. A woman is more likely to contract HIV, but this cannot explain these massive differences. And then there's maybe just actual treatment differences. So there's biological innate differences. So for example, malaria, the biggest way to prevent it is treat an insecticide-treated mosquito nets and indoor residual spraying. Is it possible? I mean, no one talks about this stuff for Africa. This stuff's always talked about in Asia. Is it possible the resource constrained households give the nets to the boys and not to the girls? We don't know. There's no work on this. Likewise, diarrheal diseases treated with solution of clean water, sugar, salt, and zinc tablets. Again, possibly they're not treating the girls as much. There's differential treatment. And of course, female decision-making power, we do know. And there is household research and household bargaining and empirical evidence that household bargaining still matters in Africa. So there is evidence that higher female autonomy in the children's health outcomes are better. There's this issue of breastfeeding. So breastfeeding is hugely important for nutrition of infants. And it's proven to significantly reduce risk of these diseases. It's found, for example, in East and Southern Africa, only 40% of babies are exclusively breastfed. So why? This would be the best prevention from these diseases. Is it possible they have inadequate support from the partners? These women, they're burdened by labor constraints. And is it possible in Africa? And we found there's a paper showing evidence of this in India that women are more likely to exclusively breastfeed boys and girls. Is this possibly going on in Africa? Again, not at all analyze these issues. And then there's possibly cultural factors at place. So for example, the tradition of early marriage. So we do know that, for example, pregnancy, people are much more susceptible to malaria if they're pregnant, women are. And it has much more detrimental consequences. And it's true that 12% of girls in sub-Saharan Africa are married before the age of 15. So they would be in our 0 to 14 age group. And for example, rates of child marriage are exceptionally high. So around 28% to 29% of places like Niger, Central Africa, and Republican Chad, these are also the countries amongst the highest rates of excess female mortality from malaria in this age. So possibly. But again, no work being done on this. And then another aspect is just the role of religion possibly. So in very traditional religious of Africa, within Africa, the archetypal institution is the patronage. So just like in the Asia story of having a son is very important, a healthy son for all the inheritance purposes and rituals and so forth. But again, this isn't the talk about typically in Africa. And also, of course, Islam has a strong grip in several parts of Africa. And there is evidence in not, and again, this hasn't been stated in Africa, but there is evidence in non-African Islamic countries that child female mortality exceeds that of males. And that isn't found elsewhere, right? Because all I'll sequel, the female child's a healthier one. And in some Islamic studies in Africa, there is evidence of boys and men. I think this might be elsewhere too. Boys and men traditionally eat first. Girls and women eat the leftovers. If there's very little food, women aren't. Girls are not getting much food at all. So this would be a reason for under-nutrition of females. Again, this is just putting up possibilities of things for future research if we believe these estimates to sort of dig deeper into these issues. So it's beyond the scope of this chapter to pinpoint a specific mechanism. But what it makes clear is that first, access to female mortality, because of vastly overlooked issue, especially with this missing women literature, if we believe these estimates, and there's massive variation across the continent, within regions, by disease. Of course, it's very hard to pinpoint a single explanation, just like it was before. So people really focused on that sex ratio birth, but it wasn't really what explaining all of the missing women that's been talked about. But essentially, these are alarming numbers. If we believe these estimates, I mean, the missing women literature, we believe them estimates for Asia, there's no reason not to believe them necessarily for Africa, unless you think the data's just so much worse. So it seems like future research into this area is crucial. These are very serious estimates and numbers. Thank you, Sivan, quite a heads up. Thank you very much.