 My question is for the first presentation for the minister. So I remember in the presentation, there was some period of time where at that time the economic growth is high and the poverty reduces is high, also the inequality also reduced. But later on, especially in early 2000, that trend is reverted for the inequality. So I want to ask, what's the main driver for this difference? Why for some time the economic growth and the inequality reduction can be achieved at the same time, but later on that was not possible? Is it because a change in economic growth model or some other reasons? Thank you very much. My comment is directed at Sussmita. I'm in a situation that I have just a couple of months ago been on a dissertation committee, which is exactly on the Kagera work. And one key issue that came up in that context was, how representative is your final sample actually? Because you have a number of things that have happened between then and now and there's a very high likelihood that the ones who have exited, the final sample actually were poorer to begin with. So I mean, if that's the case, then there is an issue. And I was just wondering whether you had pondered about it. I mean, you may well have investigated, but I believe I didn't see that in the presentation. The second point very quickly is that in that context I just mentioned, weather came out as an extremely important variable in impacting on final outcomes at the end. And I'm just wondering about whether your diversification and whether to begin with, whether they have been quite closely correlated. Because if that's the case, then there might be a case for investigating that relationship in the origin of it. Because then the sort of direct link that you have from diversification to the final outcomes might actually be, I mean, there might be something to be looked into there. Thanks. I think mine is classification from the last paper, a presenter on livelihood. Somewhere within your presentation, you indicated that it came out that everybody was diversified in a way. Then you move on to say that you're going to use that categorization where you are saying that you have one, if you are diversified or so-called diversified, the dummy variable. Yeah, so I think maybe I didn't get clearly, but I got you saying that everybody was diversified in one way or the other. Then I was wondering whether now that you're talking about income as you are a key area of diversification, whether you had in mind about rural remittances. I mean the remittances that come from, because that area is specifically a main rural area. Do you know you receive rural area sometimes in Africa receives a lot of remittances. I don't know whether you really control for that as a way of really looking through your income as you are key area. Thank you. For you, Neil, taking advantage of the fact that you're here with your experience working for the government. What specific policies do you think might best accelerate reductions in poverty and inequality in Muslim big, but also if you have any words on the Tanzania work, that would also be great. Then David, why do you think was the effect of COVID-19 so strong amongst the less vulnerable? What kind of mechanisms could have been at play, and if you have any sense for that? And then for Sushmita, you have a positive story about income diversification. But how successful can these strategies be, especially in the face of covariate shocks like climate change, or given that this is presumably, I mean mostly slum areas, these are pretty vulnerable households to covariate shocks. And like Finn, I'm slightly worried also about the attrition issue. So if you could say something about that. Okay. Thank you, Patricia. I will ask the question that one of the colleagues here arises. And of course, if needed, Professor Finn can add, because he's one of the co-authors of this study. We had in the beginning of, for this whole period that we analyzed the first period, which is in the 19 years. In fact, we had poverty reduction, and we saw the stable in terms of inequality. This is because we were coming from a very high rate in terms of poverty, because it was after the war. The country was in general without nothing in terms of education, health, economic infrastructures. So the process to build all these services, let's say, the results was a little bit in terms of rejuvenation of poverty. Because the country has to deal with strong, very strong policies in terms of social policies and economic policies, trying to push the country for a high-level rate of huge social and economic problems. But in the process continue for the 15 years in terms of building, bringing all these kind of services that the country needs, specifically in terms of social. That's why we saw the process of addressing of poverty continues. But in Mozambique, we are facing one challenge that I think that some of African countries are facing. It's the growth of the population that is growing in terms, comparative terms, very high. This is a problem for us in terms of poverty and inequality, because the government are not able to provide all these services that we was in the past able to provide in the last, let's say, 10 years. The growth population is increasing very high and very quick. This is one of the problems. And we need to transform our economy, in fact. The model that we come with from 19 years, it's not for these years the good models, because we have high levels of inequality because of the growth of the poverty, the public service in terms of health, education, it's not enough. So we are facing a lot of challenge in these recent years. That's why we are seeing the increase of these poverty and inequality. And Mozambique, it's very high vulnerability countries when we talk about external shocks, because we highly depends on imports. And when we see the problems abroad related with the prices of the commodities, all these issues affect our country. So we have to change our economic model, maybe we have to look off these, let's say, problem of growth population and other issues related with the prices of the basket of the food for our household and so on. In general terms, it's more or less this problem that we are facing. That's why you saw in the beginning of the years that we analyze it reducing and now it started to increase. But as I said, Professor Finley can add, for the question of Patricia in terms of police, I think that in Mozambique we have to work in many ways. We have to deal with a lot of issues. First, we have to invest in education. This is one of our challenge. We have to invest in all social services. I'm talking about education, health, and social protection, because we have a very, we have huge number of people that we call vulnerable people. And when we are facing a small shock, internal or external, it affects all these population that we call vulnerable population. So we have to have a strong policy in social protections that we can work and deal with these people, of course, investing in the same time in economic, especially in the economic growth, solid economic growth. I will for now stop here. Thank you. Thanks. Okay. Yes, replying to the question why the least vulnerable were most affected by COVID. So the short answer is because they happen to be related to work or to be related to the sectors that suffered the first order impacts, the first order economic impacts from the pandemic and the associated measures. So elaborating a bit what we do is we contrast our results from our study on the pre-pandemic poverty dynamics and vulnerability profiles with what we know from external sources on how COVID has affected the economy. And these external sources are mainly the economic updates of the World Bank and also the reports by the Bank of Tanzania, also the knowledge of my co-authors that are based in Tanzania. Looking at sectoral data and some small service and so on, what it emerges is that these main effects were in the tourism sector, which for obvious reasons, because international mobility was heavily restricted, and they also point specifically to urban workers, mainly to small middle enterprises and to informal urban workers, because even if the lockdown measures were very limited in the case of Tanzania, it's still a more or less generalized pattern, that these sectors are the ones that suffer more from the lockdowns because they face reduced demand and they have the, well, for informal workers, work can just be interrupted, small middle enterprises can face more difficulties to survive this period. So we know from external sources these are the sectors that are most directly affected by the economic consequences of the pandemic. Now, if we look at our results, well, it turns out that persistent poverty and also vulnerability have a very marked rural agricultural character and that especially formal workers and urban people in urban sectors, but also like, let's say, yes, so people living in rural areas with non-farm jobs or informal workers in general have a much more favorable situation at the beginning than people, for instance, in agriculture. So that's a bit like the message that emerges that in general, the people who face these first-order impacts that have been identified elsewhere are different from the people that we identify as vulnerable. One result, they didn't emphasize it that much, but where there's some overlap is export-oriented agriculture, which also seems to have suffered a lot, and there we don't see that their vulnerability patterns are in line with the national average. That's one result, one type of sector affected by COVID, where they wouldn't be better off, let's say, at the beginning, but in general, the message that emerges, what that's going to get. Thank you. So thanks, Fin, for the questions they are spot on. So about the representativeness of the data, I agree, but that's the best what we can do given the state of panel data of developing countries we have at the moment. According, we went through the data manual, data collection manual quite thoroughly, and it seems that the attrition rate of the households that were interviewed the most in the baseline, they had the least attrition. So that's the best we can offer at the moment. But of course, we have to keep this caveat in mind and reflect on this in the paper, that we do. So yeah, we have to put a discussion section on this, and then the weather in regions could affect the results. Yes, I agree. For now, with the diversification variable and also the baseline income of the main households, what we do is that we pull the income across all the four panels because they were also bi-annually collected and each probably different regions had different months. And so what we do is pull them together for the four waves and the diversification index also come from the pool data. So that's what we do to minimize the weather variability. And probably that also points out to the covariate shocks a bit. That's what we do so far. And yes, and we also have the regular controls and we have the control for at region level, like not at district level, but one tier below that. So that's what we have so far. Yes, and about the remittances. I think I just used a different word. I said rents and transfers, but we do have that as well as one of the six main income sources. So we have the income sources which are contributing to more than 10% of the total household income. And yes, remittances is one of the main sources there. Yes. And Patricia's question I partly answered. I hope. Yes. Yeah. Actually, I have not understood your answer to covariant risk. So like in the case of drought or a virus, everyone is affected. So in that case, diversification does not help. All right. Because you're assuming that over time, those liking droughts won't be rare events. But these days, they are quite common. So diversification there may not help. The other question I had is, how did you use PSM, propensity is called matching. To address the selection problem. Because you said you are using the PSM to address the selection issue. How did you do it? Yes, yes, and indeed no. I think my question is for Anilda. Just on the, I think the point you made around, you know, poverty and inequality increasing and increasing alongside the population growth that we see in Mozambique in the post-war period. And when I say war, I mean, I don't mean the current conflict, but I think post the sort of renamal issues that there were. And I think now with a lot of the natural resource discoveries that have been seen, both on the coastline and what we see also happening in global gas markets. What do you think at a policy level can be done to make sure that at least some of the upside associated with some of those discoveries and even the existing you know, gas fines in Mozambique can avert what is often called a resource curse, but also include some form of equitable distribution that can deal with some of the spatial inequalities that you are talking about. Thanks, my mind is really just sort of a comment. First on the synthetic panel, there is a parallel study on Mozambique, actually, to the one for Tanzania. And it's kind of interesting that there are some differences that do appear from sort of at least sort of looking at it to be related to the fact that Tanzania followed. Let's put it this way, quite irresponsible policies. I mean, the chair of the wider board died. And quite a number of people from the former sector in Tanzania died because they were not allowed to recognize COVID as a problem. So, I mean, and it's very interesting to see that there are some, how can I say discrepancies, if you wish to put it that way, between Mozambique and Tanzania, which probably can be traced to that. So there's an interesting sort of thing there which one might want to look into more systematically. I just want to stress that inequality has not gone down in Mozambique. Inequality has gradually been sneaking up. And then unfortunately, it now seems to be moving faster up than it has before. When it comes to poverty, the trend has basically been down, down, then flat because of international price increases. And then down again, but now up. So that's sort of the, and the last question that's raised is a very important question, raised very directly to these issues. Thanks. Okay, and I was going to suggest that maybe in the last 30 seconds, Ineal, maybe you address that question and I suggest Susmita and the gentleman there discuss the technical details of a coffee. So if you don't mind, I'll just keep the last word. Yes, thank you, Patricia. I don't have, let's say, the right answer for this question, because now we are in Mozambique, we are discussing about this new discovery and an NLNG. How the country will use properly this resource to solve the problems that we have, especially in terms of poverty, inequality, so we don't have the right question, the right model now. We are trying to see what other countries with this resource are doing, but the discussion is about if we use the model of monetary transfer directly for the household in some countries, this model works well, we have this in the table we are discussing, or we will invest, throw the budget in the infrastructures, education with the focus in the education and health. So now we think that the revenue that will come from this resource in fact will help the country to improve these indicators that are not performing well, but we still don't have the right model, but we think that it will improve if we use this resource properly with a good model. This is what I can say now, thank you. Thank you very much to all and if I ask everyone for a round of applause for our speakers. Thank you very much.