1 00:00:00,240 --> 00:00:05,880 So today it is my great pleasure to welcome Dr. Ricardo Nogales to the Tuesday seminar. 2 00:00:06,450 --> 00:00:12,300 Ricardo is a research officer at the Office of Policy and Human Development Initiatives since 2018. 3 00:00:12,660 --> 00:00:19,070 He holds a Bachelor of Science in Muscle Science and Economics and History and Econometrics, all from the University of Geneva. 4 00:00:19,080 --> 00:00:24,809 And before you joined Oxford, he was a professor of economics at the School of Economics and Finance of the. 5 00:00:24,810 --> 00:00:31,740 Was that remarkable of a young man in Bolivia and a research assistant at the United Nations Research Institute for Social Development in Switzerland, 6 00:00:31,740 --> 00:00:37,290 Switzerland. So his research focuses on development, economics, poverty reduction, human development. 7 00:00:37,650 --> 00:00:44,490 And he did quite a bit of extensive research on the different issues with the IDB, you and the ILO, World Bank and others. 8 00:00:44,850 --> 00:00:49,950 So he has been also an external consultant for several public organisations in Bolivia, 9 00:00:50,130 --> 00:00:56,130 including the Programme for Strategic Research, the Central Bank and the Ministry of Economics and Public Finance. 10 00:00:56,580 --> 00:01:00,690 His research has been published for his forthcoming annual Developmental Indicators Research, 11 00:01:00,750 --> 00:01:04,920 Latin American Research Review and Markets Find Emerging Market Funds and rates. 12 00:01:05,370 --> 00:01:10,650 So recurrent recurrent presents to date the paper that the new research conducts together with Pakistan. 13 00:01:10,870 --> 00:01:14,399 He has also from the University of Oxford on the issue of ageing, 14 00:01:14,400 --> 00:01:20,010 conflict and human deprivation in Nigeria, Democratic Republic of Congo and Ethiopia. 15 00:01:20,340 --> 00:01:25,919 So together, Ricardo and Cristian have contributed also to the conflict that from the project of 16 00:01:25,920 --> 00:01:30,059 look at the system of the centre and we've participated in some of the workshops 17 00:01:30,060 --> 00:01:34,290 and they will be also both contributing to a forthcoming special issue on different 18 00:01:34,440 --> 00:01:38,550 mythologies and methodologies to understand the changes in armed conflicts. 19 00:01:38,760 --> 00:01:41,330 And next term, also at the NSA, 20 00:01:41,340 --> 00:01:48,600 that's the CCW centre is bringing also a group of speakers together in the Tuesday seminar on topics related to the conflict platform. 21 00:01:49,500 --> 00:01:52,530 So we look forward to seeing you also next term. 22 00:01:52,530 --> 00:01:58,380 So again, a warm welcome to you, Ricardo. And now the floor is go for next 40, 45 minutes. 23 00:01:58,410 --> 00:02:02,040 Great. Thanks. Thank you very much, Marcus. And good afternoon, everyone. 24 00:02:02,040 --> 00:02:07,139 And thank you to the centre to this to Kate and to Annette for having me here today to 25 00:02:07,140 --> 00:02:12,030 present some preliminary work on something that has slightly changed in terms of titles. 26 00:02:12,360 --> 00:02:15,900 So we're trying to assess and revisit the connection between conflict and well-being, 27 00:02:16,200 --> 00:02:20,130 and we try to see to what extent we can say new things about this connection in sub-Saharan Africa. 28 00:02:20,790 --> 00:02:28,770 So as Margaret said, this is a joint work, ongoing work with my co-author, Kristin Ortigas, who's also a research officer at the University of Oxford. 29 00:02:29,490 --> 00:02:31,410 So just an overview of the presentation. 30 00:02:31,410 --> 00:02:36,720 I'm going to be presenting the main ideas behind the introduction and the background and the motivation of our study. 31 00:02:37,350 --> 00:02:45,540 Then I would be challenging how we should conceive well-being when we think about its connection to conflict and how do we define developing space. 32 00:02:46,170 --> 00:02:51,450 Of course, I'm going to go through concepts and methods and of course data because that's going to be a very important limiting factor. 33 00:02:51,960 --> 00:02:57,360 Then I'm going to go a little bit deeper into the connection itself, talking about the nature of conflict and the data availability. 34 00:02:57,750 --> 00:03:03,209 And then I'm going to show you some preliminary results. They're going to have two kinds of results. 35 00:03:03,210 --> 00:03:09,270 First, we're going to have a graphical representation first, and I'm going to go a little bit deeper into some kind of model based analysis. 36 00:03:10,290 --> 00:03:13,290 And then, of course, the concluding remarks are just thoughts at this stage. 37 00:03:13,290 --> 00:03:19,950 And then I hope that they open the floor for some nice discussion and and debates around the things that I'm going to put on the table today. 38 00:03:20,940 --> 00:03:27,480 So I guess it's very well known for all of us that armed conflict precedes the social tissue and it reshapes people's lives in many ways. 39 00:03:27,960 --> 00:03:33,690 And we have a lot of evidence when it comes to cross-national comparisons that focus on monetary aspects of welfare, 40 00:03:33,990 --> 00:03:38,400 mainly GDP or the same economic making machine when it comes to money. 41 00:03:38,970 --> 00:03:42,690 And you have very interesting works related on that. 42 00:03:43,080 --> 00:03:49,890 And it's a it's a very valid and ongoing strand of research when it comes to the connection between policy and conflict. 43 00:03:50,670 --> 00:03:54,720 But then you also have a growing strand of research that says that a lot of 44 00:03:54,960 --> 00:03:58,080 aspects of people's lives are reconfigured not only in their purchasing power, 45 00:03:58,530 --> 00:04:05,099 such as, for example, you have a very nice study by Marconi and co-authors who find negative effects. 46 00:04:05,100 --> 00:04:07,950 Of course, in terms of school attendance in Nigeria because of Boko Haram, 47 00:04:08,340 --> 00:04:13,350 you also have some long term negative effects in Vietnam related to mental health issues. 48 00:04:13,770 --> 00:04:19,919 And then you also have a little bit more of a connect, a connecting mechanism when it comes to conflict inducing job loss. 49 00:04:19,920 --> 00:04:23,879 And that's being the cause of school dropouts in Palestine, for example. 50 00:04:23,880 --> 00:04:26,340 There's a connecting mechanism to recover change if you want. 51 00:04:27,300 --> 00:04:33,090 So I guess that you have more and more evidence showing that when you focus on monetary aspects of poverty, 52 00:04:33,090 --> 00:04:37,860 you only get a thin overview of what's happening with people's lives when it relates to conflict. 53 00:04:38,790 --> 00:04:42,509 But then also you see that there are other things that are going on because sometimes you see 54 00:04:42,510 --> 00:04:47,520 that conflict reshapes the social teaching in a way that you have some emerging markets, 55 00:04:47,700 --> 00:04:53,939 informal markets, that the kind of setting place where you would actually see that a certain aspect of people's 56 00:04:53,940 --> 00:04:57,660 lives or purchasing power is actually not protected because it's not the right word, 57 00:04:58,050 --> 00:05:03,990 but the the extent to which it. Hindered by conflicts can actually be a little bit mitigated because of this informal 58 00:05:04,000 --> 00:05:08,680 teachers and and institutions that are really configured in conflict areas. 59 00:05:09,370 --> 00:05:14,890 So with this very preliminary research overview, literature overview, 60 00:05:15,370 --> 00:05:22,870 we ask what if we actually take into account the very intuitive fact that when a person or a household experiences conflict, 61 00:05:23,320 --> 00:05:28,420 they're not only purchasing power, not only school attendance, not only mental health are affected. 62 00:05:28,420 --> 00:05:29,740 So they're all affected at the same time. 63 00:05:30,460 --> 00:05:37,150 So one thing that is actually not necessarily very prevalent in this kind of studies is the interconnection or the simultaneous nature of this effect. 64 00:05:37,720 --> 00:05:41,920 So they tried to focus very rightfully in one specific aspect, one at a time, 65 00:05:41,920 --> 00:05:46,600 and they allow for a very rich understanding of what happens with one particular aspect. 66 00:05:47,020 --> 00:05:53,710 But that amounts to actually thinking of well-being as a cumulative aspect of, let's say, isolated deprivation. 67 00:05:54,370 --> 00:05:58,120 And you don't take into account the fact that they often manifest themselves so continuously, 68 00:05:58,120 --> 00:06:01,870 so you don't take into account the depth or the intensity of the operations. 69 00:06:02,650 --> 00:06:07,780 So in that context, in this paper, we argue to what extent do the experience of simultaneous well-being, 70 00:06:07,780 --> 00:06:15,399 the provisions can be related to armed conflict? So what we're challenging is the fact that the evaluative space can be broken up and 71 00:06:15,400 --> 00:06:21,370 can be in region to come up with other things that are not necessarily looking there, 72 00:06:21,640 --> 00:06:26,560 that are not necessarily looked at, but they're not requiring the new data, to be honest. 73 00:06:27,400 --> 00:06:33,040 Of course, we're not trying to draw in generalities because we do acknowledge that every context is very, very different. 74 00:06:33,490 --> 00:06:37,420 But we try to see to what extent we can have cross-national comparisons to the extent possible. 75 00:06:37,840 --> 00:06:43,330 So that's why we focus on empirical examples of this three cases which were chosen for many reasons. 76 00:06:43,660 --> 00:06:46,180 One of them, I have to acknowledge that it's the availability, 77 00:06:46,360 --> 00:06:51,940 but also because they have similar levels of welfare deprivation according to community standards. 78 00:06:52,120 --> 00:06:56,380 So we can actually have some kind of welfare provisions that start from a similar aspect. 79 00:06:58,630 --> 00:07:03,790 So if we challenge then the concept of well-being, then how do we conceptualise that? 80 00:07:04,150 --> 00:07:08,770 I think it's very intuitive and very easy to understand that it is multifaceted in nature 81 00:07:08,770 --> 00:07:13,210 because you have a lot of strength in terms of policymaking and international policy agendas, 82 00:07:13,660 --> 00:07:16,780 but very highly, very, very powerful, powerful pitch. 83 00:07:16,810 --> 00:07:21,280 The fact that poverty, for example, is linked to the national well-being is multidimensional. 84 00:07:21,790 --> 00:07:26,619 That is reflected in the eggs, for example, in Agenda 2030, where Goal one, for example, 85 00:07:26,620 --> 00:07:32,710 is not to reduce poverty by monetary terms, but it is to reduce poverty in all its forms and dimensions, whatever that may be. 86 00:07:33,670 --> 00:07:40,210 You do have that also reflected in more qualitative studies, such as the one conducted by the World Bank, which is named the voices of the Poor. 87 00:07:40,660 --> 00:07:45,220 We're actually people were asked how they live in general when they face poverty, 88 00:07:45,550 --> 00:07:52,160 and they actually said that many more things are lacking in their lives other than just not having money in the pocket. 89 00:07:53,350 --> 00:07:59,440 When you think of the quality of life in this sense. Stiglitz Because the commission that started thinking about how to replace in a 90 00:07:59,440 --> 00:08:04,450 certain way GDP as a measure of development have also adopted these point of view. 91 00:08:04,870 --> 00:08:12,250 You have the Atkinson report and then of course you have measures that have tried to privatise this region to say that the quantity is wrong. 92 00:08:12,490 --> 00:08:22,570 Well, such as UNDP's HDI. Now, when we think about what the dimensionality and we're willing to accept that that is the case. 93 00:08:22,840 --> 00:08:33,940 Then what do we mean by that term? A lot of academic research actually think that multidimensional accounting means accounting for a lot of aspects. 94 00:08:35,260 --> 00:08:40,540 At the same time, that is looking at the dashboard of things like we're looking at health, 95 00:08:40,540 --> 00:08:43,990 we're looking at education, we're looking at work, looking at money. 96 00:08:44,260 --> 00:08:50,320 We're looking at a dashboard of things. That's only one part of the problem because I think some very powerful thing 97 00:08:50,860 --> 00:08:54,940 actually what the dimensionality has to take into account not only many aspects, 98 00:08:55,210 --> 00:08:59,410 but also how they are connected in terms of more quantitative aspects. 99 00:08:59,860 --> 00:09:04,689 You have to take into account the joint distribution of the provisions are not necessarily single isolated 100 00:09:04,690 --> 00:09:10,819 marginal distributions because they come from a joint distribution in terms of statistical valuation. 101 00:09:10,820 --> 00:09:16,450 But is important because the space of evaluation is different. If we take into account I'm going to show you one example. 102 00:09:16,900 --> 00:09:23,080 If we take into account of the isolated distribution of one thing and we don't connected with the isolated distribution of other things, 103 00:09:23,080 --> 00:09:26,890 we're missing out. Now, there are many ways of doing that. 104 00:09:27,130 --> 00:09:31,450 I'm going to present the one that we work with at the research centre here at the university. 105 00:09:31,690 --> 00:09:35,740 It's not the only one I'm happy to discuss afterwards, but perhaps that's the subject for differences, 106 00:09:36,910 --> 00:09:41,380 which is called the apparent cluster method, and it's a part of the family of the dual accounting approach. 107 00:09:41,390 --> 00:09:48,160 I'm going to explain how that works in a way for you to have a good grasp of what exactly would postulating as the biology of space, 108 00:09:48,430 --> 00:09:50,920 of well-being, of connection to conflict. 109 00:09:51,760 --> 00:09:58,690 Now, this method, of course, is the one that used to be and we use to measure multidimensional poverty all over the developing world. 110 00:09:59,140 --> 00:10:05,600 But it is. Also the underlying technique for UNICEF multi multiple overlapping information analysis, UNICEF similar. 111 00:10:06,140 --> 00:10:11,660 And it is also the one that the World Bank is using to construct what they call the multidimensional poverty measure. 112 00:10:13,700 --> 00:10:18,140 So what does this mean? Say, for example, that you have two regions in a country. 113 00:10:18,770 --> 00:10:25,460 So you have region one and you have region. There is a similar amount of conflict if you want in both regions, 114 00:10:27,470 --> 00:10:33,770 say we accept multidimensional poverty and we think that four aspect of relevance to assess the effect of conflict on people's lives, 115 00:10:34,190 --> 00:10:40,970 income, education, shelter and water. And we see here that in every row we have, say, a household. 116 00:10:41,420 --> 00:10:46,580 So we have a society composed of two regions where in each region we have four people for households. 117 00:10:47,690 --> 00:10:51,230 Now the D here stands for Deprived, so they fall below the poverty line. 118 00:10:51,680 --> 00:10:54,410 They say nobody is educated in the households. 119 00:10:54,710 --> 00:11:00,530 They don't have adequate shelter by, say, energy standards, and they don't have access to safe drinking water. 120 00:11:03,500 --> 00:11:06,650 So that's what this means for they deprived. 121 00:11:07,040 --> 00:11:12,410 And these stands for non deprived. So we don't detect that they have fallen, say, below a certain threshold. 122 00:11:13,730 --> 00:11:20,810 So if you see the marginal distributions and you assess what is the proportion of people who are deprived in income or the poverty rate, 123 00:11:21,230 --> 00:11:26,930 you would conclude that one out of four households are depressed. So you would come up with some kind of a hit ratio of 25%. 124 00:11:27,110 --> 00:11:31,640 Right. Which is exactly what happens in this society. It is also 25%. 125 00:11:32,090 --> 00:11:37,790 If you take into account what is the proportion of people who lack education or who are deprived of education by certain standards? 126 00:11:38,120 --> 00:11:41,120 Again, you will find that one fourth or 25% of prevalence. 127 00:11:41,420 --> 00:11:47,239 And you will also find the same conclusion here. I mean, the and these examples shows that if you take marginal distributions, 128 00:11:47,240 --> 00:11:50,389 you will never be able to detect differences in those distributions because you will 129 00:11:50,390 --> 00:11:55,459 always come to the conclusion that both of them are deprived in 25% in each dimension. 130 00:11:55,460 --> 00:12:05,210 Isolated. Right. But then also. But then, of course, if you only have to look at it for a couple of seconds to realise that that's not true. 131 00:12:05,300 --> 00:12:10,950 Because one thing that is completely overlooked is the fact that intensity of poverty is different here. 132 00:12:10,980 --> 00:12:18,530 The provisions, if you want, are distributed evenly among the population, whereas here they're completely concentrated in one household. 133 00:12:19,160 --> 00:12:19,400 Right. 134 00:12:20,060 --> 00:12:28,040 So that intensity of poverty is not cannot be uncovered unless you take a different route and you try to establish the extent to which deprivations 135 00:12:28,040 --> 00:12:35,330 are connected and whether the likelihood of one manifesting itself is linked or not to the likelihood of other ones being manifested as well. 136 00:12:36,260 --> 00:12:40,610 So that is the reason or that is the reasoning behind the Spatial Foundation. 137 00:12:40,880 --> 00:12:44,870 We're not saying that the proportion of people deprived in each aspect is not important. 138 00:12:44,900 --> 00:12:50,299 Of course it is. But we have to go a little bit beyond that to assess the depth of poverty or the asset, 139 00:12:50,300 --> 00:12:56,990 the debt of deprivation, what we think of how conflict is associated with with well-being in general. 140 00:12:58,700 --> 00:13:03,860 Because, of course, I mean, not only for the sake of understanding the problem, but also because in terms of policy making, 141 00:13:04,010 --> 00:13:08,420 of course, policymaking is radically different in this situation or in this situation. 142 00:13:08,810 --> 00:13:15,770 Right. Presumably, targeting, for example, is much more difficult here because the provisions are scattered across the population, 143 00:13:15,770 --> 00:13:21,320 whereas here targeting is easier in a way because we only have to find the one person who suffers from everything. 144 00:13:22,110 --> 00:13:26,780 Yeah. So how does he. How does this work in this method that we propose? 145 00:13:27,620 --> 00:13:34,909 So we basically we have no more households and we ended up with different key elements 146 00:13:34,910 --> 00:13:38,930 when it comes to thinking about what we want to evaluate as a measure of well-being. 147 00:13:39,740 --> 00:13:45,950 So the first would be and that's why it's called the counting approach, you can actually count the number of deprivations that each person faces. 148 00:13:46,340 --> 00:13:49,670 Here, for example, you would have zero the provisions faced by the first household. 149 00:13:50,120 --> 00:13:53,180 Here you have two deprivations faced by the second household. 150 00:13:53,540 --> 00:13:58,370 You have for it deprivations faced by the third. And then you have one deprivation faced by the other. 151 00:13:59,360 --> 00:14:06,440 So once you count the deprivations, you can redefine the people or the households who you understand to be poor. 152 00:14:07,160 --> 00:14:11,510 So, for example, in this case, you need to set up poverty. So the World Bank, for example, 153 00:14:11,510 --> 00:14:19,970 says poor is somebody who doesn't make or who doesn't spend more than one night a day or housing doesn't spend more than one day. 154 00:14:20,810 --> 00:14:25,550 That is a poverty. So here, for example, we can say poor are going to be poor. 155 00:14:25,730 --> 00:14:32,330 Household is going to be a household where you have two or more deprivations that are observed. 156 00:14:32,870 --> 00:14:36,770 Or you can have three or more deprivations. Or you can have one or more deprivations. 157 00:14:36,830 --> 00:14:39,200 So you can have different poverty length. Right. 158 00:14:40,190 --> 00:14:46,459 I'm not going to enter into the debate of how we set the poverty line, because it's a different problem when it comes to poverty measurement. 159 00:14:46,460 --> 00:14:50,600 But we're going to adopt a measure that already has a poverty line adopted by UNDP. 160 00:14:50,900 --> 00:14:54,770 I'm going to mention it. But of course, if you can if you want, we can discuss it afterwards. 161 00:14:54,860 --> 00:14:59,480 So here, for example, in this in this slide, we just for for the sake of. 162 00:14:59,590 --> 00:15:06,909 Of the illustration, we just adopted a pavilion of two, so a household would be considered poor if they face two out of the four deprivations, 163 00:15:06,910 --> 00:15:09,160 so they are deprived in half of the considered indicators. 164 00:15:10,240 --> 00:15:16,870 Now, this is the reason why they are these people are highlighted because they are the poor households in the society. 165 00:15:17,680 --> 00:15:22,840 Now the question can come. So in this case, the incidence of multidimensional poverty would be 50%, whatever. 166 00:15:23,950 --> 00:15:30,430 Now the question arises as to how poor they are, because of course, they're both poor, but they're both poor with different intensities. 167 00:15:30,730 --> 00:15:34,990 This household. The third one is much poorer than the first one. 168 00:15:35,020 --> 00:15:41,830 Say that the one the first poorer one here. So we can actually measure the average number of deprivations faced by the poor. 169 00:15:42,340 --> 00:15:45,850 So here, for example, this person, this household is depriving two out of four. 170 00:15:46,030 --> 00:15:51,340 So they have an intensity for the provision of 50% and this household is depriving 44. 171 00:15:51,460 --> 00:15:54,430 So they have the intensity of deprivation of 100%. 172 00:15:55,120 --> 00:16:02,710 So the average of those numbers is going to be the average intensity of multidimensional poverty in that society, which is 75%. 173 00:16:03,220 --> 00:16:11,170 It means that on average of poor households in this society is expected to face 75% of deprivations that they can face. 174 00:16:11,890 --> 00:16:14,740 Okay. So it's a measure of depth of multidimensional poverty. 175 00:16:15,880 --> 00:16:21,250 And once you see that, of course, these two aspects, which are both important, can be combined in a composite index, 176 00:16:21,250 --> 00:16:27,400 which is called the Multidimensional Poverty Index, properly set, which is a simple multiplication of both. 177 00:16:27,670 --> 00:16:34,180 So it's an index that takes into account both intensity and incidence of poverty to 178 00:16:34,180 --> 00:16:38,470 judge the overall amount of poverty that you can find in the society if you want. 179 00:16:40,000 --> 00:16:43,780 So this new element, as far as we know in the literature, 180 00:16:43,780 --> 00:16:52,090 have not been investigated and contrasted with conflict because we do see a lot of very weak analysis when it comes to isolated deprivation. 181 00:16:52,330 --> 00:17:00,430 But this element of the depth or the proportion of of deprivation space by households is not necessarily investigated to a similar extent. 182 00:17:01,720 --> 00:17:08,530 So this is precisely what he said to do in the paper. And for this, we take into account three countries. 183 00:17:08,560 --> 00:17:14,350 So if you, Pierre, Congo and Algeria, and to measure this these well-being aspects, 184 00:17:14,350 --> 00:17:22,179 we draw upon the demographic and health surveys conducted by U.S., U.S. aid in developing countries for one particular reason. 185 00:17:22,180 --> 00:17:28,840 And it is the international comparability and harmonisation of culture. Of course, it's not the only source of that kind of information. 186 00:17:29,380 --> 00:17:35,380 It is perhaps not the best, but it is certainly the one that assures to a greater extent, comparability between countries. 187 00:17:36,640 --> 00:17:41,709 If we were to conduct, for example, a study specifically on Nigeria or specifically in Ethiopia, 188 00:17:41,710 --> 00:17:48,730 then perhaps we can have other options that are more suitable to cover different aspects more comprehensively for those particular areas. 189 00:17:49,690 --> 00:17:52,840 Now, of course, many aspects can be covered, 190 00:17:53,260 --> 00:17:58,360 and we're drawing upon one particular version of of this global multidimensional 191 00:17:58,360 --> 00:18:03,010 poverty index that is the one that produces each year for the developing world. 192 00:18:03,910 --> 00:18:11,650 Basically, the U.N. and US, we construct every year for every developing country a multinational poverty index that takes into account health, 193 00:18:11,920 --> 00:18:13,540 education and living standards. 194 00:18:14,500 --> 00:18:21,670 In health, we take into account the nutritional aspects of the household as measured by the Z scores to detect malnutrition among children. 195 00:18:22,600 --> 00:18:26,889 Child mortality occurring in the last five years preceding the survey and under-five mortality. 196 00:18:26,890 --> 00:18:34,480 Of course, years of schooling and school attendance are indicators of education, years of schooling in terms of schooling attainment, 197 00:18:34,480 --> 00:18:41,770 if you want, and school attendance in terms of children who are deprived of their access to school or just being out of school for some reason. 198 00:18:42,460 --> 00:18:45,910 And then when it comes to living standards, we have six indicators cooking fuel, 199 00:18:45,910 --> 00:18:53,550 and we consider a household to be deprived if they use some kind of solid fuels because of health concerns, improve sanitation. 200 00:18:53,560 --> 00:18:57,969 We follow energy standards to detect whether or not sanitation that are available for 201 00:18:57,970 --> 00:19:02,590 the household are compliant with what is considered to be improved by energy standards. 202 00:19:03,010 --> 00:19:07,210 The same thing with safe drinking water access to electricity. 203 00:19:07,510 --> 00:19:14,530 Flooring is also a very important aspect when it comes to health, and it's related to living conditions because of course, if you have a dirt floor, 204 00:19:14,530 --> 00:19:20,650 for example, you are much more prone to get some kind of diseases and you will have negative spill-overs in terms of health, 205 00:19:21,310 --> 00:19:28,300 when it comes to asset ownership, we try to take into account basic livelihood assets such as a refrigerator or for communication purposes, 206 00:19:28,570 --> 00:19:38,139 a radio or TV or a landline or a phone, or for, say, being able to travel. 207 00:19:38,140 --> 00:19:44,830 We consider any kind of motorbike or bicycle or truck or some kind of vehicle that allows people to move. 208 00:19:45,580 --> 00:19:47,440 So those are the ten indicators that we consider. 209 00:19:48,070 --> 00:19:52,720 Again, that could be a subject of debate, but are we trying to shield ourselves from that for the time being? 210 00:19:53,170 --> 00:19:58,870 Because this is one option that is already published in international work for international comparisons using different. 211 00:20:00,820 --> 00:20:08,110 So that is for the wellbeing aspect of the work when it comes to the conflict aspect of the work. 212 00:20:08,390 --> 00:20:14,980 Now of course if wellbeing is multidimensional, conflict is again multidimensional. 213 00:20:14,980 --> 00:20:22,930 Of course there are many aspects of conflict. So we try to draw on on a net framework to conceptualise and perceive what are the aspects of 214 00:20:23,440 --> 00:20:30,160 conflict that we want to do to contrast our measure of well-being with and her framework. 215 00:20:30,430 --> 00:20:33,550 I don't know if you know the framework. I'm sure you do. 216 00:20:33,850 --> 00:20:38,680 And if not, perhaps I'm not the best person to explain that. Perhaps you know much more than me. 217 00:20:39,280 --> 00:20:42,790 But you have a very rich framework where she analyses four different aspects or 218 00:20:42,790 --> 00:20:46,150 different aspects of conflict that are changing across time and across space. 219 00:20:46,720 --> 00:20:48,129 So basically you would have different groups. 220 00:20:48,130 --> 00:20:52,360 For example, you have different forms of conflict, you have different means and you have different places. 221 00:20:52,720 --> 00:20:56,379 And of course, you will have different consequences based on data limitations. 222 00:20:56,380 --> 00:21:02,110 We're not able to take into account every single aspect of conflict that she proposes in a very comprehensive framework. 223 00:21:02,620 --> 00:21:06,810 But we're trying to see whether or not we detect differences in terms of the form of conflict. 224 00:21:06,820 --> 00:21:10,629 So we analyse different forms of conflicts, we analyse of course, different places. 225 00:21:10,630 --> 00:21:14,350 And I'm going to get into that in a different in a different slide. 226 00:21:14,770 --> 00:21:18,370 And of course we heavily draw on when it comes to the consequences. 227 00:21:18,370 --> 00:21:23,110 We actually challenge the fact that consequences can be overlooked if we take different strengths, 228 00:21:23,310 --> 00:21:32,280 we take different approaches that are would be thinner or maybe more simplistic when it comes to assessing where we are now in terms of differences. 229 00:21:32,290 --> 00:21:35,950 This is at an exploratory stage. We're trying to draw on equity. 230 00:21:36,250 --> 00:21:45,110 We are well aware of its limitations, but also when asked to admit that it is one of the datasets that is perhaps one of the most known in economics. 231 00:21:45,110 --> 00:21:52,419 I'm an economist in economics literature. It is very widely spread, it is very widely used, even though it is based from the journals. 232 00:21:52,420 --> 00:21:59,649 And we know exactly what limitations lie. But since we're trying to explore what we can say in terms of this connection of of frameworks, 233 00:21:59,650 --> 00:22:03,550 then we just adopted that for the preliminary exercise. 234 00:22:04,360 --> 00:22:12,670 So here you have Nigeria. The darker colours represent higher levels of poverty as measured by our proposal. 235 00:22:13,330 --> 00:22:17,670 So you would have higher intensity and proportion of people who are poor. 236 00:22:18,010 --> 00:22:23,400 So we would take into account not only the proportion of people who are poor, but also how poorly fits the combination of both. 237 00:22:24,400 --> 00:22:30,610 The darker the colour, the poorer the region. So you can see that of course in Nigeria the northern regions are the poorest. 238 00:22:31,150 --> 00:22:37,360 Yeah, and they're also very concentrated in Borno. They're also regions where you have a lot of ongoing conflict here. 239 00:22:37,360 --> 00:22:42,820 The size of the bubble is representative of the fatalities associated to each conflict and each thought is a conflict. 240 00:22:43,480 --> 00:22:50,470 Okay, so here we try to see is what kind of unit of analysis we can actually use for our for our paper. 241 00:22:52,360 --> 00:23:02,020 We are limited by the HSC. So the wellbeing data, the lowest level that we can attain in this case is the 37 states of Nigeria. 242 00:23:02,890 --> 00:23:09,520 So we have sample. So data has been collected from every single state and it has it is representative at that level. 243 00:23:10,090 --> 00:23:15,309 We would actually go to have a more granular perspective and a more people centric perspective, 244 00:23:15,310 --> 00:23:18,280 but of course we're not allowed to say what's happening with people. 245 00:23:18,790 --> 00:23:23,630 Yeah, we can only say what's happening on average in a certain region because of data sensitivity, right? 246 00:23:24,070 --> 00:23:29,870 So the unit of analysis can be the region. We know that that's troubling because what border do you do? 247 00:23:29,870 --> 00:23:37,450 You put it into the conflict. So borders are fuzzy. So we try to see to what extent we can account for that in the it is a model based analysis, 248 00:23:38,020 --> 00:23:43,600 but we do have for the sake of data limitations, we do have to respect the level of representative duty of the data. 249 00:23:43,720 --> 00:23:47,110 So our unit of analysis has to be the political boundaries in the region. 250 00:23:47,980 --> 00:23:57,250 And we do see that we have data even from the most conflicted areas that is intended to be representative because of what's happening now. 251 00:23:57,460 --> 00:24:00,790 This is the evolution of conflict and poverty in Nigeria. 252 00:24:01,120 --> 00:24:08,710 So you can see that over the five years that I present here, poverty has been constantly being elevated in in the northern regions. 253 00:24:09,040 --> 00:24:16,060 It has maintained it has always been lower in the south regions, in Lagos here, for example, and conflict has been ongoing. 254 00:24:16,690 --> 00:24:22,150 Over the regions. You will see that we detect a change in the levels of poverty by 2017. 255 00:24:22,600 --> 00:24:27,940 Yeah, where we have slight movements, but in the end the situation has not changed dramatically. 256 00:24:28,510 --> 00:24:35,200 Basically in our study, in a separate study, Nigeria out of the three countries is the only one for which we don't detect any kind of 257 00:24:35,200 --> 00:24:41,080 evolutions in terms of welfare or well-being as measured by this this framework that we present. 258 00:24:41,590 --> 00:24:48,100 So basically the level of well-being has been stagnant, but it has been stagnant both in the poorer regions and in the least poor regions. 259 00:24:49,810 --> 00:24:52,690 When it comes to the future, for example, we see a different configuration. 260 00:24:53,110 --> 00:24:57,069 We see that poverty, of course, is much more prevalent in the Somali region. 261 00:24:57,070 --> 00:25:00,719 I mean a far but then. When it comes to detecting changes. 262 00:25:00,720 --> 00:25:06,300 Actually, the whole country has reduced poverty overall. So they have improved even though conflict was ongoing. 263 00:25:06,700 --> 00:25:12,270 Yeah, I mean, you can see that conflict was really prevalent even in in Oromia where it's different. 264 00:25:12,960 --> 00:25:16,020 Of course, it's the least poor region, but it is very prevalent in terms of conflict. 265 00:25:16,320 --> 00:25:19,620 But it has maintained its status as being one of the least poor regions in the country. 266 00:25:20,400 --> 00:25:28,470 Yeah, when it comes to the Congo, for example, you see a different picture, you see a much more homogeneous aspect when it comes to poverty. 267 00:25:28,860 --> 00:25:34,680 They're all very poor. But then you also see that poverty has been reconfigured over the years with dictators. 268 00:25:35,010 --> 00:25:44,070 And you can see that well in the books, for example, or cite or in Oriental or even Oriental, for example, poverty has actually reduced. 269 00:25:44,800 --> 00:25:49,350 Yeah. So you can see that here. For example, we see a positive change when it comes to welfare. 270 00:25:49,380 --> 00:25:56,250 In in, in Oriental where there are so many conflicts in the key that we cannot see how it moved, but it actually remains. 271 00:25:57,420 --> 00:26:00,880 So of course, what we can say with this in terms of numbers, 272 00:26:00,900 --> 00:26:06,900 so how do we relate this when it comes to actual significance and differences when it comes to regions? 273 00:26:08,010 --> 00:26:14,770 So I will very briefly explain this because I know that this is an interesting part for a lot, but we basically say, 274 00:26:14,770 --> 00:26:20,790 look, what we can do is we can have a unit of analysis which is the subnational region because of data limitations. 275 00:26:22,290 --> 00:26:28,649 However, we would be very wrong to say that conflict in one region affects people only in that 276 00:26:28,650 --> 00:26:32,070 region or that people in one region is only affected by conflict in that region. 277 00:26:32,640 --> 00:26:36,180 No, because in the end, the whole country is connected by economic systems. 278 00:26:36,600 --> 00:26:44,249 Right. So we try to cover the extent to which we can have spatial spill-overs and we try to see whether or not we 279 00:26:44,250 --> 00:26:49,409 can detect whether neighbouring regions are actually affecting conflict occurring in neighbouring regions, 280 00:26:49,410 --> 00:26:57,810 are affecting poverty status in in the region itself outside of the proper direct effect of conflict in that precise region. 281 00:26:58,590 --> 00:27:00,090 So that is what these models sense for. 282 00:27:00,420 --> 00:27:05,580 So here I'm sure that perhaps most of you would recognise this equation where this would be the dependent variable, 283 00:27:05,850 --> 00:27:09,810 which is in turn the intensity, the incidence or the combination of both. 284 00:27:11,310 --> 00:27:18,840 Here you have the number of conflicts in different periods in time. So here I make the difference between the moment that we were measuring welfare, 285 00:27:18,840 --> 00:27:23,610 which is T and then s the moment of the conflict, which can be actually before. 286 00:27:23,700 --> 00:27:30,720 So we have a time dependence. So as is any time in any period in time that is lower than or equal. 287 00:27:31,630 --> 00:27:35,070 And then here you have an element that is very special to the spatial balance, 288 00:27:35,550 --> 00:27:41,000 which accounts for not only conflict occurring in Region I, but the vector of conflicts. 289 00:27:41,220 --> 00:27:44,820 Sorry, but the vector of conflict occurring in all the country. 290 00:27:45,660 --> 00:27:50,640 Yeah. So at one point in time, this is why we omits the individual index here. 291 00:27:51,060 --> 00:27:54,800 And then these conflicts are activated through a weighting matrix that is, 292 00:27:54,810 --> 00:27:59,970 and an end by end matrix that activates conflicting neighbouring regions for different points in time. 293 00:28:00,810 --> 00:28:06,990 This is the efficient that captures spatial spill-overs. If this coefficient is significant at a certain level, 294 00:28:07,410 --> 00:28:13,890 then we can see and we can detect whether or not conflicts in neighbouring regions have an effect, 295 00:28:14,880 --> 00:28:18,890 say pure or net of the direct effect of conflict in that position region. 296 00:28:19,680 --> 00:28:24,030 So that is the way we can account for this kind of spatial spill-overs, if you want, 297 00:28:24,240 --> 00:28:30,030 while respecting the unit of analysis, the data mandate, without breaking the physical activity of the data. 298 00:28:31,650 --> 00:28:36,910 So I hope that that's clear. Then I guess that we can go back to that if you want. 299 00:28:36,960 --> 00:28:42,180 And then here, I'm just going to present some preliminary results of. We do have much more results, 300 00:28:42,190 --> 00:28:47,339 but I'm going to focus on a couple that already point to the fact that there is 301 00:28:47,340 --> 00:28:52,800 something to say and we try to distil and be more putting it much more into context. 302 00:28:53,670 --> 00:28:59,190 So here, for example, we focus on conflict side one, conflict side one, our battles according to a clear definition. 303 00:29:00,060 --> 00:29:07,680 And we first don't take into account any kind of spatial, spatial spill-overs, and we consider that only direct effects. 304 00:29:07,680 --> 00:29:11,910 So conflicts happening in one region have an effect on the livelihood of people in that region. 305 00:29:12,180 --> 00:29:14,610 So we are oblivious to the spill-over. 306 00:29:16,680 --> 00:29:24,960 Here you have poverty as measured or well-being, a lack of well-being as measured by the proportion of people who live in poverty, 307 00:29:25,830 --> 00:29:29,190 the intensity at which they experience poverty, and a combination of both. 308 00:29:29,850 --> 00:29:35,790 Here you have the countries that are represented in different colours. The North represents the main point, the main estimate. 309 00:29:36,460 --> 00:29:42,150 The lines represent the confidence intervals of that estimate. And if they touch zero, they're not they're not in the cross zero. 310 00:29:42,150 --> 00:29:48,570 They're not significant. And this is on a 95% confidence in the horizontal axis, you have the lack. 311 00:29:48,720 --> 00:29:51,750 So like zero means that we take into account contemporaneous conflicts. 312 00:29:52,020 --> 00:29:57,180 Like one means conflicts occurring the year before data for well-being was observed. 313 00:29:57,930 --> 00:30:00,810 Lack two represents. Two years before that and so on and so forth. 314 00:30:01,980 --> 00:30:08,910 So what we can see here, for example, is that, first of all, we do have we detect a lot of kind of contemporaneous effects in terms of patterns. 315 00:30:09,450 --> 00:30:15,089 So conflicts occurring in one particular point in time have a very strong, significant effect or have a very strong, 316 00:30:15,090 --> 00:30:19,799 significant association in this case with levels of well-being as measured by the 317 00:30:19,800 --> 00:30:23,430 proportion of people and as measured by the intensity at which they suffer poverty. 318 00:30:24,210 --> 00:30:26,580 But then we also see that, of course, longer term, 319 00:30:26,940 --> 00:30:33,480 longer conflicts are occurring a longer period in time in the past have to have some effect, but to a much lower extent. 320 00:30:34,290 --> 00:30:37,830 What is interesting here, for example, is if you see what happens in Congo. 321 00:30:38,490 --> 00:30:44,850 So if we try to measure well-being by the proportion of people who suffer poverty, then we would reach no conclusion. 322 00:30:45,480 --> 00:30:53,130 So we would see that actually the proportion of people who face poverty has not been changed, not by contemporary conflicts, nor by post-conflict. 323 00:30:53,970 --> 00:30:58,530 But if you see what happens with the intensity at which poor people suffer poverty, it has actually increased. 324 00:30:59,370 --> 00:31:04,860 So that already tells us something about the kind of people who are affected by this particular type of conflict occurring in Congo. 325 00:31:05,310 --> 00:31:08,480 It is not necessarily more people who are going to be falling into poverty. 326 00:31:08,490 --> 00:31:13,050 So it is not people who are at the verge of falling into poverty because they're around the poverty line. 327 00:31:13,560 --> 00:31:18,090 But it is mostly affecting the intensity of people who are already below the poverty line, 328 00:31:18,090 --> 00:31:24,120 are suffering and not only from the contemporaneous conflicts, but also for the ones that occurred in the past. 329 00:31:24,870 --> 00:31:31,020 So this you cannot see if you don't disentangle these effects and if you don't take into account the joint distribution that I was mentioning before. 330 00:31:32,190 --> 00:31:38,100 So we can also see that in the case of Ethiopia, Nigeria, for example, that the effect goes both sides. 331 00:31:38,520 --> 00:31:43,770 So it not it's not only associated to the fact that you have more proportion of people living in poor areas, 332 00:31:44,040 --> 00:31:49,590 but it is also associated to the fact that people who were already poor are suffering from poverty in terms of intensity. 333 00:31:51,000 --> 00:31:57,540 This is what we find with no spatial spill-overs for conflict, that if we change the form of conflict, then we get different results. 334 00:31:57,540 --> 00:32:01,440 And that's where we try to engage with amidst a rich framework about how to define conflict. 335 00:32:02,340 --> 00:32:09,690 If we take the concept of conflict and we measure conflict not necessarily by battles but by riots and protests, then we get a different picture. 336 00:32:10,470 --> 00:32:13,740 In the case of if you look, for example, we don't expect any kind of effect. 337 00:32:13,980 --> 00:32:19,350 We have a large variation, but we don't detect any kind of effect. And in the case of Nigeria, for example, 338 00:32:19,350 --> 00:32:24,780 we see that this type of conflict has actually been more associated with an increase 339 00:32:24,780 --> 00:32:29,069 of the proportion of people being poor rather than people who were already poor, 340 00:32:29,070 --> 00:32:33,450 being less poor or being being poor in terms of intensity. 341 00:32:34,470 --> 00:32:37,830 So the effect seems to be much more significant in terms of the proportion. 342 00:32:38,880 --> 00:32:45,840 So of course, we have to dig into that. But we think that that means that a different kind of population is affected by this type of conflict, 343 00:32:45,840 --> 00:32:49,590 that these for people who are perhaps nearer to the poverty. 344 00:32:50,280 --> 00:32:54,140 Yeah. Now, this is with no specialist groups. 345 00:32:54,570 --> 00:33:02,550 What if we take into account that borders are fuzzy and that conflicts in neighbouring regions can actually affect people living in other regions, 346 00:33:02,850 --> 00:33:07,350 which is likely to be the case? But we are trying to see whether we can capture that with the data that we have. 347 00:33:08,730 --> 00:33:13,700 So here this is a little bit more complicated, but so in colour in. 348 00:33:14,170 --> 00:33:17,070 Yes. That's why I'm showing some results. Because we have many of them. 349 00:33:18,690 --> 00:33:25,410 In rows we have the proportions, the effects or the associations with the proportion of people living in poverty. 350 00:33:26,070 --> 00:33:33,690 Yeah. Here in the second row, we have the intensity of poverty as measured by the proportion of the average proportion of deprivations that they face. 351 00:33:33,690 --> 00:33:38,009 And here we have the combination of both. So those are the rows in columns. 352 00:33:38,010 --> 00:33:41,250 You have the countries, you have Congo, you have your period of Nigeria. 353 00:33:42,390 --> 00:33:47,820 The colours here now represent the black. So in this case, green represents contemporaneous conflicts. 354 00:33:48,960 --> 00:33:57,660 This colour orange represents represents conflicts occurring the year before and so on. 355 00:33:58,140 --> 00:34:02,820 Okay. Then you separate what we call the direct effects from the indirect effects. 356 00:34:03,340 --> 00:34:10,770 Yeah. The indirect effects are effects caused or associations caused by not conflict occurring in that region, 357 00:34:10,920 --> 00:34:19,160 by conflict occurring in the neighbouring regions. Right. So in Congo we don't detect any kind of neighbouring effects or spatial spill-overs. 358 00:34:19,740 --> 00:34:25,110 But in the future for example, you do see a short term effect, which is quite significant in terms of indirect effects. 359 00:34:25,620 --> 00:34:28,890 So you see that neighbouring regions in Ethiopia, sorry, 360 00:34:29,010 --> 00:34:33,389 one region in Ethiopia on average tends to suffer from conflict occurring in the neighbouring regions as well. 361 00:34:33,390 --> 00:34:41,280 To a great extent when it comes to contemporaneous conflicts, then the the extent of the indirect effects reduces over time. 362 00:34:41,430 --> 00:34:47,430 So we see a large indirect short term effect of conflict type one that means battles in Ethiopia. 363 00:34:48,240 --> 00:34:52,050 It's interesting to see that in Nigeria, for example, we see a much longer standing. 364 00:34:53,010 --> 00:34:58,710 So we see that conflict in neighbouring regions. Not only affects the welfare state, it's often. 365 00:34:58,750 --> 00:35:06,190 People in other regions contemporaneously, but also we detect very strong, significant indirect effects over time. 366 00:35:06,430 --> 00:35:14,590 So it is a more persisting time of type of indirect effect when it comes to changing, again, the nature of conflict. 367 00:35:14,770 --> 00:35:22,390 And we think of what happens with neighbouring conflicts, but not as measured by battles, but as measured by riots and protests. 368 00:35:23,080 --> 00:35:28,540 Then again, in Congo we don't detect any kind of spatial spill-overs in Ethiopia. 369 00:35:28,810 --> 00:35:34,990 We detect some kind of spatial spill-overs, but again, only contemporaneous, not over time. 370 00:35:35,470 --> 00:35:38,680 And then again, in Nigeria, this type of conflict seems to be very prevalent, 371 00:35:38,770 --> 00:35:43,299 but only in the short term, not necessarily in the long term now, which kind of makes sense. 372 00:35:43,300 --> 00:35:46,360 But we have to dig a little bit more into that and contextualising results that we get. 373 00:35:47,440 --> 00:35:58,390 So this is the kind of results that we are able to produce by combining both this new aspect of, of, of well-being measure. 374 00:35:58,450 --> 00:36:03,969 So one thing that I forgot to mention is so one thing that is important here is that the effects, for example, 375 00:36:03,970 --> 00:36:10,750 the spill-over effect here is as strong in terms of the proportion of people as it is for the intensity of poverty. 376 00:36:11,380 --> 00:36:15,250 So it not only pushes people, say, below the poverty line, 377 00:36:15,730 --> 00:36:20,320 but it also intensifies the extent to which they experience poverty in terms of indirect effects. 378 00:36:21,620 --> 00:36:28,209 Right. So we do see that there are some things that can be said when we think about these welfare aspects, 379 00:36:28,210 --> 00:36:35,720 and we don't only focus on one particular aspect, which is monetary related, but we focus on an array of things that affect people's lives. 380 00:36:35,740 --> 00:36:39,040 Or are you confident, precise? And we assess the interconnections. 381 00:36:40,000 --> 00:36:45,579 We're also seeing that and its framework is very relevant in the sense that how you define conflict or what 382 00:36:45,580 --> 00:36:50,390 aspect of conflict you actually think will have an effect on the kind of results that you have to make. 383 00:36:50,410 --> 00:36:56,290 Because if you define some kind of violent protest, for example, a conflict or a battle as conflict, 384 00:36:56,590 --> 00:36:59,620 then you will have different results because there are different forms of conflict, of course. 385 00:37:00,850 --> 00:37:07,790 And then we also see that you can actually say that with existing secondary data. 386 00:37:08,380 --> 00:37:16,300 Of course, much more can be said that if we go into the field and if we conduct some very, very precise, in-depth analysis of each particular case. 387 00:37:17,050 --> 00:37:21,960 But what is interesting here and the point that we want to make is that you already have some kind of data available, 388 00:37:21,970 --> 00:37:24,460 which, of course, is not perfect and it has limitations, 389 00:37:24,820 --> 00:37:31,370 but it allows you to see a little bit more than you can currently find in the nature in terms of the association between conflict and. 390 00:37:32,620 --> 00:37:39,880 So some concluding remarks. Again, I think that one aspect that we want to highlight in the paper when it's going to be written, 391 00:37:40,180 --> 00:37:46,059 is the fact that the link between conflict and intensity of poverty needs to be more carefully assessed because for the time being, 392 00:37:46,060 --> 00:37:53,920 we're seeing if the status of somebody not being able to go to school has changed of somebody not having a mental problem developing one. 393 00:37:54,340 --> 00:38:01,450 But we don't see the extent to which people who already face this kind of problems have actually improved have actually deteriorated. 394 00:38:01,450 --> 00:38:07,629 Their situation even worse or we don't see the extent to which several aspects of people's lives have been reconfigured and 395 00:38:07,630 --> 00:38:14,560 conflict has simultaneously affected purchasing power and school attendance and flooring and access to drinking water, 396 00:38:14,560 --> 00:38:19,510 etc. Yet we also detect indirect effects. 397 00:38:19,660 --> 00:38:24,780 I guess that this is not necessarily a novelty per se, because we already know that, you know, 398 00:38:24,970 --> 00:38:31,150 conflict does not necessarily conform to political boundaries, not not subnational, even not internationally. 399 00:38:31,900 --> 00:38:37,690 But then what can we say if we only have data that is representative at certain subnational levels? 400 00:38:38,110 --> 00:38:43,300 It's a mistake to try to go beyond that. And that's disrespect to the same time frame, and you cannot do that. 401 00:38:43,810 --> 00:38:49,330 So one way of doing that is by acknowledging that the units that you can define as unit of 402 00:38:49,330 --> 00:38:53,410 analysis that are correct can actually have spill-over effects to their neighbouring regions. 403 00:38:53,440 --> 00:39:01,900 That is one way of taking that into account. And actually spatial models have been or are being increasingly used in this kind of research. 404 00:39:02,110 --> 00:39:08,200 And indeed there are some things that can be said using this kind of frameworks that these are more essential. 405 00:39:09,070 --> 00:39:15,879 Of course, we want to extend this study to other types of conflict and say qualified conflicts, because for the time being, 406 00:39:15,880 --> 00:39:25,240 the results that I mentioned are related to the number of conflicts, but that's a crude measure for conflict needs to be qualified in many respects. 407 00:39:25,780 --> 00:39:31,300 So we tried to come up with a cleaner, quantifier or qualifier of conflict, for example, 408 00:39:32,560 --> 00:39:38,379 using fatalities or other aspects to try to refine these measure that we have that 409 00:39:38,380 --> 00:39:43,450 for the time being very crude or the actors who perpetrated the the conflict. 410 00:39:44,260 --> 00:39:52,540 And we'll of course, we want to assess what happens if we adopt these models and this framework to other data, perhaps in the actual data, 411 00:39:52,630 --> 00:39:58,690 but other types of data that if you use, you see the usage, for example, which of course we need to reconstruct. 412 00:39:59,150 --> 00:40:03,340 The way the types of conflict that we can envision because the kind of leader that we have is different. 413 00:40:04,180 --> 00:40:10,390 I think this will see as some kind of entry door or a preliminary analysis to more in-depth analysis. 414 00:40:10,600 --> 00:40:16,210 Because one thing that we do not want to claim in the paper is that this is a generalisation of what happens in, 415 00:40:16,990 --> 00:40:22,100 say, sub-Saharan Africa or in the context. So it is very complex happening because conflict. 416 00:40:22,690 --> 00:40:27,610 Each country and each conflict is fundamentally different structurally, and it has a different history. 417 00:40:28,180 --> 00:40:33,970 So we first want to have a regional perspective, but what we can say and what is the context of each particular country. 418 00:40:34,390 --> 00:40:39,610 But then, of course, that strategic analysis will shed much clearly on the evolution of that country. 419 00:40:40,480 --> 00:40:49,690 And then even if we can, we can see the extent to which this approach can be operationalised through experimental approaches. 420 00:40:50,320 --> 00:40:56,800 But that, again, requires digging very deep into one specific case and not necessarily trying to uncover cross-national differences. 421 00:40:57,700 --> 00:41:05,080 And, of course, we don't want to merely describe what happens, but we also want to want to understand the mechanism to which that happens. 422 00:41:05,470 --> 00:41:11,920 So we want to assess and dig a little bit more into the economic and political mechanisms that are reconfigured for the results to make sense. 423 00:41:12,550 --> 00:41:16,990 So we try to not only remain of the low hanging fruit of just describing the effort, 424 00:41:16,990 --> 00:41:21,010 we try to see to what extent we can actually link our results to policy and 425 00:41:21,010 --> 00:41:24,610 economic mechanisms that are reconfigured in the period of analysis that we can. 426 00:41:25,600 --> 00:41:30,670 So as I mentioned, this is preliminary research. We are very excited to lead this forward. 427 00:41:30,700 --> 00:41:36,549 And again, thank you for giving us a chance to present this in this platform where we actually meet. 428 00:41:36,550 --> 00:41:42,820 And we will value a lot of comments and feedback from you who are experts much more than perhaps we are in this subject. 429 00:41:43,180 --> 00:41:43,990 So thank you very much.