 Let's open the floor to a discussion then. If you could, when you ask a question, just introduce yourself and also state which presenter you're addressing your question to. I might group several questions together and then respond to them in a collective way. And there is a roaming mic somewhere. Great. Have a question, two questions on that. I enjoyed all the papers, so thank you very much. I have just two comments, kum questions. The first one is on the Yuzuf Sumner paper. Basically, what you're dealing with is a dynamic system, which is based on the structural transformation. And what is very clear is that the share of employment in agriculture as well as the share of GDP in agriculture is going to fall over the course of the structural transformation. What happens in terms of other sectors depends very much on the initial conditions in terms of globalization as well as the policies followed by the government. And in this slide, it's very clear that the development model in Africa is going to be very different from the development model that was so successful in East Asia and Asia in general. Now, my question is, I had the feeling sometimes that you assumed some separability. Instead of looking at the whole complex dynamic model, which is really interdependent, you were looking at different sectors as if there was possibility of making some of these sectors more important than others. So what I would like to see is the specification of a dynamic model based on initial conditions as well as policies. On the very good presentation of James and Sabina, first of all, I feel that clearly poverty is multidimensional. So any attempt at coming up with some kind of an indicator of multidimensional poverty is a very important exercise. What I was wondering about, and of course they know this from previous meetings, there is still a lot of arbitrariness in any attempt at a multidimensional indicator. The various dimensions are considered to be more or less equal cut-off ratios tend to be arbitrary. And I was wondering if through more work on sensitivity analysis, for instance, taking different cut-off ratios, perhaps making certain dimensions relatively more important than others, one could, and maybe with the help of focus group, come up with a specification which would make it possible to add some weight to the various dimensions and also give us more confidence in terms of the cut-off deprivation levels. Thank you. Thanks. I think we had another question, a few raised behind that. Thank you very much for the interesting presentations. Javier Jara from the University of Essex. On the first presentation, I was wondering to which extent the role of the tax benefit system was left behind the scenes. And the role of the tax benefit system was left behind the scenes. And it's actually this which would be considered important in terms of how to react in reducing poverty as one of the goals. One of the things I was thinking of perhaps was why not try to decompose the genie into the contribution of tax benefit policies and the contribution of all other components? And that might give us an idea in the equation. Another way to do it was to ask ourselves whether under the current budget of each country, each country is allocating social assistance. For instance, we are targeting well those who are considered the poor and how much would poverty be reduced if we could target better and then take the patterns of projections in terms of economic growth and population growth and see how much or to which extent tax benefit policies should cope, should adapt in order to achieve the goal. On the last presentation, my comment was really in line to the previous comment in terms of something was mentioned very quickly and briefly was that all dimensions are equally valued. And I was wondering to which extent this is a strong assumption and mainly putting into the context of the literature by Mark Florbet that has been rising during these last years. To which extent should we account for the fact that different individuals might value different life dimensions in a different way? Great, thanks. So if we just take the question on the back row as well and then we'll respond to those together. Rolf and the Huffin of the Institute of Social Studies. Also, thanks for the papers. I'd like to comment on the first paper by Andrea Cornia. I think he very nicely with more variables showed that if you have a situation where the poverty line is high and very high in relation to per capita income, it's indispensable that you need growth in order to reduce poverty. And we found that already 20 years ago, but you put more variables in it to make that point. My point, my question is more on policy. In your last simulation, you add only 1% of growth to the IMF projections, and then you come to results which are better but not acceptable yet. If you look at some African countries, they have shown very high growth rates. I point to Ethiopia, to Rwanda. So the question is, why did you restrict yourself to an experiment with only 1% more additional growth? Why didn't you include experiments with more growth rates, a bit more robust? This is completely in line with the sustainable development goals with some of the goals. We don't need to go into that. Thank you. Great, thanks. We're just as fun to these questions and then go back for another round. Unless your question is directly related. OK, go for it. Well, as we all know, everything is related. After all, I'm an ecologist. Well, I think we all agree that poverty is the reason for poverty is multidimensional. But I'm wondering whether in this direction we have considered the influence of culture in poverty, on poverty. For example, let's take nutrition. You might have some cultures which do not have taboos about eating certain foods, and that might affect their nutritional intake. Or have we considered external influences on the economies and poverty? For instance, if you compare Norway and a country like Goa, the government of Norway derives about 98% of its oil revenue for the country itself and the people of Norway. So they're just thinking rich. While in the case of Goa, about 90 or more percent goes to the investors, like a company in the UK. And the peanut is given to the people of Goa. And so they remain poor. The other aspect which seems to me was not covered is the impact of manipulating the indices of poverty and improving equality, the level of equality, the influence of doing that on the environment. I think this is a very fundamental question. Because for example, if you improve agricultural production, you might need to cut down large areas of forest if you improve export crops from agriculture. Again, you do the same thing. Like they are doing in Indonesia, Malaysia, other Asian countries, including India, and so on. So we have to really consider this because we did consider population growth, which is important. But also population growth at the local level can extend into destruction of the environment and climatic change, et cetera. Great. Thank you for those really interesting questions. James, do you want to start at the end as the last question is for you? No, but that's fine. I'll be called it. The idea of normalizing indices to make it more salient between zero, one versus between odd numbers and so on, I agree that that can be quite helpful. And in fact, if you go back to the FGT2, one of the problems is the numbers just don't make sense. But if you take into a square root of FGT2, numbers start making a lot more sense and looking like equally distributed equivalents. And so I've thought for a long time that that's such a nice way of proceeding. But then immediately you jump up against decomposability. So you want to keep decomposability so you trade off simplicity. As far as the other measure is concerned, I'm not entirely sure whether normalization would help in that case it might, but I'm all game to explore that. More confidence because the cutoffs might be arbitrary. Anything in poverty is arbitrary for goodness sake at the end of the day. And so I don't have as much problem with arbitrariness compared to other people, I guess. But on the other hand, pretty much all the estimates that have come out from every country and from any global MPI now has been subjected to a ton of sensitivity analysis. And this is now par for the course and standard across the board. So this is now happening, what you've suggested is happening as we speak. I point out that there's an index called the Better Job Index at the Inter-American Development Bank that uses exactly the same technology of the MPI. You can change your weights right there online and see what happens to job quality or employment conditions. So it's quite amazing what you can now do online interactively. As far as, let's see, should all equally valued? No, they're not, just the example was. The approach can do anything you want. I have seen that there's natural tendency to do nested equal, which means go to the main dimensions and give them equal weight because they are equally important. And then if you happen to have different numbers of indicators, well, keep the weight on the dimension the same, but make equal the weight within so that you don't screw up this weighting across dimensions. So that's now the way that people have been doing it. It's a natural way of proceeding, but you get to do what you want. Sheila added 10% to another indicator when they threw it in. I actually don't understand Flora Bay's approach. And if someone can explain it to me, I'd be very happy to look it over. It's a different entity in a sense because we're talking about policy here and policy then gets together to set the target for the country or for groups of countries. And that can be informed by individual preferences and individual things. But at the end of the day, you have to choose something and go with it. Okay, finally manipulating, oh, culture. Yes, if you got data, we'll go for it, incorporate it into all that we're measuring. The missing dimensions is the second half of OFI, finding the data to incorporate things that are really important, including environmental, including context. And if you have these environmental externalities and can measure it so that we understand what's going on and the incentives that are, we're into it. It's really interesting for us. So we'll be happy to accommodate in terms of measurement if we can only get those missing dimensions out and establish as part of the data set. That's it that I'll say. So I'll leave it to you all. Thank you. On Paula's note, perhaps, I think both of the questions raised to our particular, to our presentation, I think both are challenge of extending our works. And on Paula's notes, I think it's very relevant challenge especially for Indonesia because in relation to our data, because we are using district data, so it's sub-national level, where after 2000, for example, the decentralization reform Indonesia has moved the responsibility of several important policy, such as education and health to those district sectors. So that's very relevant. And also I would like to add, I read one paper and also confirm with our data, for example, that access to higher educations is pretty much highly correlated with inequality. Indonesia, pretty much highly, but higher education, not other educations. So thanks for the challenge. And on Eric's, actually, first of all, I would like to thank Eric for the comments. I'm honored. Why? Because Eric is a supervisor of my supervisors at the Australian National National University, for a Booty Research Center. That's why that made me my incident. So yeah, I think, yeah, we have to look at, I know that our paper is lacking of framework. Actually, I think we should extend the framework of the work into more dynamic kind of work. So I think we'll try to look into it. And then maybe when we get it, we can come back to it later. And then maybe, I think we'll add something. I mean, I think there is a basic methodological choice. And when, first of all, we just wanted to look at the eradication of poverty. So the question raised by Paola, Italiana? Boliviana. Boliviana, okay. So the question is, if you want to include health education and so on and so forth. And since we are using in-challot net incomes, distributions, and then there are these big databases. I mean, you get what you can get. But man, these are the best data we can do. So if, for instance, if I have a better distribution or better targeting of benefits, Gini will be lower, okay? If I have more expenditure on education, well, particularly if it is well targeted, Gini will be lower. Same with health and so on and so forth. So there is an issue that the fact of the multidimensionality comes through via the level of the Gini and the growth rate to GDP, basically. And we added, and then of course, separate the population issue. So if you have a good public health cum family planning system and will be lower. So the variables that these are the mid-determinants which are capturing the fact of many other dimensions. And this goes back to a long, long ago when Martin, I don't know if you are right or wrong, but I think that you claim, and other people including myself claim, that if you took the correlation between infant mortality in those days, there was a video on it. And Gini, then you do find that you have an R of 07, 0809. Perhaps not everything is there. So, and then I didn't estimate any equation. I just used a decomposition. And the decomposition uses two, three parameters which I took from theoretical distributions. So the effect of health education and so on and so forth transit via GDP growth rate. And except one, because these are all done, all the calculation are done in current prices. And then there's all this literature that showing that if there is a changes in relative prices between food and non-food, actually the poor are much more penalized. And so then you have to take into account that there are at least six or seven papers including one which we have done. So this is the key point. And otherwise what should have been and actually in the paper which is available, we say, okay, we do a multi-equation system in which we have on the left-hand side poverty education and then SDG number two, number three, number four, number five and we'll find a lot of interaction between these indicators. So I didn't really feel there was a need in this particular case to have multi-dimensionality because the effects of better health, better education and so on and so forth, they do reflect themselves to a very large degree. It's not like R equal one, but R equal a lot, a very high degree. So this is the basic premise. So when I talk about immediate determinants, this is the logic of it. And now I think that why did we limit the growth rate of the African cancers to plus 1% on top of what the IMF say? Well, because the IMF came up with this data. And here I tend to agree with the findings on Indonesia because we find on Africa in another study we did for UNDP Africa. We do find that a large part of the explanation of the increasing inequality in Africa is the wrong pattern of structural transition. The share of agriculture falls, the share of manufacturing falls. That's the drama. Normally you say you go to a low inequality sector which is agriculture unless you have Latifundia and the white settlers and so on and so forth. Then you move to simple and skilled labor-intensive manufacturing. These are the low gene. We have data for 18 African countries on a panel. And then actually you jump into modern services, very high gene, or the informal services sector, including government, which you have very high gene. So this is the non-Rostovian. I mean, I'm not making publicity for Walter Rostow, but I mean, I think that it's very difficult to claim that Africa will develop by concentrating more on agriculture and on the tertiary sector. And so there is a major problem of who's going to do manufacturing in Africa? This is the question. And the domestic industry has been declining in line with trade liberalization. Actually who captures that? Well, the Vietnamese, the Chinese, the Indians and so on and so forth. So is that fair? Is it useful for inequality in Africa? No, I don't think it is. Now, perhaps the foreign direct investments, now the Chinese are transplanting some Hualien, who is one of the major cheap shoe producer, moved to Ethiopia and wages in China are, I don't know, 400 a month and in a disability they are 50 a month. So perhaps, but is it safe to have an industrialization entirely in the foreign direct investment? It's a bit risky because tomorrow there may be some other countries offering better conditions. So I think that when I say, well, we increase the growth rate of Africa by 1%. This is, I think is, I don't want to say optimistic, but I mean, the growth rate of Africa basically has been depending on commodity prices. And commodity prices have been high and now they go up and down. And so the IMF in his own super wise assessment has come up with idea that over up to 2022 and then we extended it will, so there is this basic issue of reprimarization in Africa and Latin America. And if you read Jose Antonio Campo, actually you see that the argument is, which he made, is that, well, I mean, the developing countries, let's say Africa, the commodity producer, Latin America and Africa will develop fast enough to eradicate poverty. If basically China de-links to the growth of Western, of the OECD country, China exports a lot of commodities, sorry, a lot of manufactured goods to the US and to Europe. If now the trade war is successful, I mean, in the sense that Trump gets his own way, then actually there's economies will slow down and they will import fewer commodities from Africa, fewer commodities from Latin America and the growth rate in these two countries which have reprimarized because share manufacturing is falling and will slow down as well. So 1% is not too pessimistic growth, I think, is in from this perspective. And I think that this is, so the methodologically, the key issue is that the facts of health, education and so on so forth are reflected in genes and growth rates. If you are healthy, you will produce more. Man, this micro-primary literature. And then on how much more to raise the growth rate of Africa and particularly of Africa is basically, I think, risky. I mean, it's not obvious that with the trends in the global economy, Africa will export more commodities. And I think it's wrong that Africa and Latin America should limit themselves to the exportation of primary commodities, to the commodity producers, to the manufacture producers. Thank you. Well, apologies, they've run out of time for another round of questions, but I'm sure all the presenters will be happy to speak to you and answer any questions you have over the course of the next few days. So in the meantime, all that's left today is, thank you very much for attending the session. Thank you.