So today it is my great pleasure to welcome Dr. Ricardo Nogales to the Tuesday seminar.
Ricardo is a research officer at the Office of Policy and Human Development Initiatives since 2018.
He holds a Bachelor of Science in Muscle Science and Economics and History and Econometrics, all from the University of Geneva.
And before you joined Oxford, he was a professor of economics at the School of Economics and Finance of the.
Was that remarkable of a young man in Bolivia and a research assistant at the United Nations Research Institute for Social Development in Switzerland,
Switzerland. So his research focuses on development, economics, poverty reduction, human development.
And he did quite a bit of extensive research on the different issues with the IDB, you and the ILO, World Bank and others.
So he has been also an external consultant for several public organisations in Bolivia,
including the Programme for Strategic Research, the Central Bank and the Ministry of Economics and Public Finance.
His research has been published for his forthcoming annual Developmental Indicators Research,
Latin American Research Review and Markets Find Emerging Market Funds and rates.
So recurrent recurrent presents to date the paper that the new research conducts together with Pakistan.
He has also from the University of Oxford on the issue of ageing,
conflict and human deprivation in Nigeria, Democratic Republic of Congo and Ethiopia.
So together, Ricardo and Cristian have contributed also to the conflict that from the project of
look at the system of the centre and we've participated in some of the workshops
and they will be also both contributing to a forthcoming special issue on different
mythologies and methodologies to understand the changes in armed conflicts.
And next term, also at the NSA,
that's the CCW centre is bringing also a group of speakers together in the Tuesday seminar on topics related to the conflict platform.
So we look forward to seeing you also next term.
So again, a warm welcome to you, Ricardo. And now the floor is go for next 40, 45 minutes.
Great. Thanks. Thank you very much, Marcus. And good afternoon, everyone.
And thank you to the centre to this to Kate and to Annette for having me here today to
present some preliminary work on something that has slightly changed in terms of titles.
So we're trying to assess and revisit the connection between conflict and well-being,
and we try to see to what extent we can say new things about this connection in sub-Saharan Africa.
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.
So just an overview of the presentation.
I'm going to be presenting the main ideas behind the introduction and the background and the motivation of our study.
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.
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.
Then I'm going to go a little bit deeper into the connection itself, talking about the nature of conflict and the data availability.
And then I'm going to show you some preliminary results. They're going to have two kinds of results.
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.
And then, of course, the concluding remarks are just thoughts at this stage.
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.
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.
And we have a lot of evidence when it comes to cross-national comparisons that focus on monetary aspects of welfare,
mainly GDP or the same economic making machine when it comes to money.
And you have very interesting works related on that.
And it's a it's a very valid and ongoing strand of research when it comes to the connection between policy and conflict.
But then you also have a growing strand of research that says that a lot of
aspects of people's lives are reconfigured not only in their purchasing power,
such as, for example, you have a very nice study by Marconi and co-authors who find negative effects.
Of course, in terms of school attendance in Nigeria because of Boko Haram,
you also have some long term negative effects in Vietnam related to mental health issues.
And then you also have a little bit more of a connect, a connecting mechanism when it comes to conflict inducing job loss.
And that's being the cause of school dropouts in Palestine, for example.
There's a connecting mechanism to recover change if you want.
So I guess that you have more and more evidence showing that when you focus on monetary aspects of poverty,
you only get a thin overview of what's happening with people's lives when it relates to conflict.
But then also you see that there are other things that are going on because sometimes you see
that conflict reshapes the social teaching in a way that you have some emerging markets,
informal markets, that the kind of setting place where you would actually see that a certain aspect of people's
lives or purchasing power is actually not protected because it's not the right word,
but the the extent to which it. Hindered by conflicts can actually be a little bit mitigated because of this informal
teachers and and institutions that are really configured in conflict areas.
So with this very preliminary research overview, literature overview,
we ask what if we actually take into account the very intuitive fact that when a person or a household experiences conflict,
they're not only purchasing power, not only school attendance, not only mental health are affected.
So they're all affected at the same time.
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.
So they tried to focus very rightfully in one specific aspect, one at a time,
and they allow for a very rich understanding of what happens with one particular aspect.
But that amounts to actually thinking of well-being as a cumulative aspect of, let's say, isolated deprivation.
And you don't take into account the fact that they often manifest themselves so continuously,
so you don't take into account the depth or the intensity of the operations.
So in that context, in this paper, we argue to what extent do the experience of simultaneous well-being,
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
can be in region to come up with other things that are not necessarily looking there,
that are not necessarily looked at, but they're not requiring the new data, to be honest.
Of course, we're not trying to draw in generalities because we do acknowledge that every context is very, very different.
But we try to see to what extent we can have cross-national comparisons to the extent possible.
So that's why we focus on empirical examples of this three cases which were chosen for many reasons.
One of them, I have to acknowledge that it's the availability,
but also because they have similar levels of welfare deprivation according to community standards.
So we can actually have some kind of welfare provisions that start from a similar aspect.
So if we challenge then the concept of well-being, then how do we conceptualise that?
I think it's very intuitive and very easy to understand that it is multifaceted in nature
because you have a lot of strength in terms of policymaking and international policy agendas,
but very highly, very, very powerful, powerful pitch.
The fact that poverty, for example, is linked to the national well-being is multidimensional.
That is reflected in the eggs, for example, in Agenda 2030, where Goal one, for example,
is not to reduce poverty by monetary terms, but it is to reduce poverty in all its forms and dimensions, whatever that may be.
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.
We're actually people were asked how they live in general when they face poverty,
and they actually said that many more things are lacking in their lives other than just not having money in the pocket.
When you think of the quality of life in this sense. Stiglitz Because the commission that started thinking about how to replace in a
certain way GDP as a measure of development have also adopted these point of view.
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.
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.
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.
At the same time, that is looking at the dashboard of things like we're looking at health,
we're looking at education, we're looking at work, looking at money.
We're looking at a dashboard of things. That's only one part of the problem because I think some very powerful thing
actually what the dimensionality has to take into account not only many aspects,
but also how they are connected in terms of more quantitative aspects.
You have to take into account the joint distribution of the provisions are not necessarily single isolated
marginal distributions because they come from a joint distribution in terms of statistical valuation.
But is important because the space of evaluation is different. If we take into account I'm going to show you one example.
If we take into account of the isolated distribution of one thing and we don't connected with the isolated distribution of other things,
we're missing out. Now, there are many ways of doing that.
I'm going to present the one that we work with at the research centre here at the university.
It's not the only one I'm happy to discuss afterwards, but perhaps that's the subject for differences,
which is called the apparent cluster method, and it's a part of the family of the dual accounting approach.
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,
of well-being, of connection to conflict.
Now, this method, of course, is the one that used to be and we use to measure multidimensional poverty all over the developing world.
But it is. Also the underlying technique for UNICEF multi multiple overlapping information analysis, UNICEF similar.
And it is also the one that the World Bank is using to construct what they call the multidimensional poverty measure.
So what does this mean? Say, for example, that you have two regions in a country.
So you have region one and you have region. There is a similar amount of conflict if you want in both regions,
say we accept multidimensional poverty and we think that four aspect of relevance to assess the effect of conflict on people's lives,
income, education, shelter and water. And we see here that in every row we have, say, a household.
So we have a society composed of two regions where in each region we have four people for households.
Now the D here stands for Deprived, so they fall below the poverty line.
They say nobody is educated in the households.
They don't have adequate shelter by, say, energy standards, and they don't have access to safe drinking water.
So that's what this means for they deprived.
And these stands for non deprived. So we don't detect that they have fallen, say, below a certain threshold.
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,
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%.
Right. Which is exactly what happens in this society. It is also 25%.
If you take into account what is the proportion of people who lack education or who are deprived of education by certain standards?
Again, you will find that one fourth or 25% of prevalence.
And you will also find the same conclusion here. I mean, the and these examples shows that if you take marginal distributions,
you will never be able to detect differences in those distributions because you will
always come to the conclusion that both of them are deprived in 25% in each dimension.
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.
Because one thing that is completely overlooked is the fact that intensity of poverty is different here.
The provisions, if you want, are distributed evenly among the population, whereas here they're completely concentrated in one household.
Right.
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
are connected and whether the likelihood of one manifesting itself is linked or not to the likelihood of other ones being manifested as well.
So that is the reason or that is the reasoning behind the Spatial Foundation.
We're not saying that the proportion of people deprived in each aspect is not important.
Of course it is. But we have to go a little bit beyond that to assess the depth of poverty or the asset,
the debt of deprivation, what we think of how conflict is associated with with well-being in general.
Because, of course, I mean, not only for the sake of understanding the problem, but also because in terms of policy making,
of course, policymaking is radically different in this situation or in this situation.
Right. Presumably, targeting, for example, is much more difficult here because the provisions are scattered across the population,
whereas here targeting is easier in a way because we only have to find the one person who suffers from everything.
Yeah. So how does he. How does this work in this method that we propose?
So we basically we have no more households and we ended up with different key elements
when it comes to thinking about what we want to evaluate as a measure of well-being.
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.
Here, for example, you would have zero the provisions faced by the first household.
Here you have two deprivations faced by the second household.
You have for it deprivations faced by the third. And then you have one deprivation faced by the other.
So once you count the deprivations, you can redefine the people or the households who you understand to be poor.
So, for example, in this case, you need to set up poverty. So the World Bank, for example,
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.
That is a poverty. So here, for example, we can say poor are going to be poor.
Household is going to be a household where you have two or more deprivations that are observed.
Or you can have three or more deprivations. Or you can have one or more deprivations.
So you can have different poverty length. Right.
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.
But we're going to adopt a measure that already has a poverty line adopted by UNDP.
I'm going to mention it. But of course, if you can if you want, we can discuss it afterwards.
So here, for example, in this in this slide, we just for for the sake of.
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,
so they are deprived in half of the considered indicators.
Now, this is the reason why they are these people are highlighted because they are the poor households in the society.
Now the question can come. So in this case, the incidence of multidimensional poverty would be 50%, whatever.
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.
This household. The third one is much poorer than the first one.
Say that the one the first poorer one here. So we can actually measure the average number of deprivations faced by the poor.
So here, for example, this person, this household is depriving two out of four.
So they have an intensity for the provision of 50% and this household is depriving 44.
So they have the intensity of deprivation of 100%.
So the average of those numbers is going to be the average intensity of multidimensional poverty in that society, which is 75%.
It means that on average of poor households in this society is expected to face 75% of deprivations that they can face.
Okay. So it's a measure of depth of multidimensional poverty.
And once you see that, of course, these two aspects, which are both important, can be combined in a composite index,
which is called the Multidimensional Poverty Index, properly set, which is a simple multiplication of both.
So it's an index that takes into account both intensity and incidence of poverty to
judge the overall amount of poverty that you can find in the society if you want.
So this new element, as far as we know in the literature,
have not been investigated and contrasted with conflict because we do see a lot of very weak analysis when it comes to isolated deprivation.
But this element of the depth or the proportion of of deprivation space by households is not necessarily investigated to a similar extent.
So this is precisely what he said to do in the paper. And for this, we take into account three countries.
So if you, Pierre, Congo and Algeria, and to measure this these well-being aspects,
we draw upon the demographic and health surveys conducted by U.S., U.S. aid in developing countries for one particular reason.
And it is the international comparability and harmonisation of culture. Of course, it's not the only source of that kind of information.
It is perhaps not the best, but it is certainly the one that assures to a greater extent, comparability between countries.
If we were to conduct, for example, a study specifically on Nigeria or specifically in Ethiopia,
then perhaps we can have other options that are more suitable to cover different aspects more comprehensively for those particular areas.
Now, of course, many aspects can be covered,
and we're drawing upon one particular version of of this global multidimensional
poverty index that is the one that produces each year for the developing world.
Basically, the U.N. and US, we construct every year for every developing country a multinational poverty index that takes into account health,
education and living standards.
In health, we take into account the nutritional aspects of the household as measured by the Z scores to detect malnutrition among children.
Child mortality occurring in the last five years preceding the survey and under-five mortality.
Of course, years of schooling and school attendance are indicators of education, years of schooling in terms of schooling attainment,
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.
And then when it comes to living standards, we have six indicators cooking fuel,
and we consider a household to be deprived if they use some kind of solid fuels because of health concerns, improve sanitation.
We follow energy standards to detect whether or not sanitation that are available for
the household are compliant with what is considered to be improved by energy standards.
The same thing with safe drinking water access to electricity.
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,
for example, you are much more prone to get some kind of diseases and you will have negative spill-overs in terms of health,
when it comes to asset ownership, we try to take into account basic livelihood assets such as a refrigerator or for communication purposes,
a radio or TV or a landline or a phone, or for, say, being able to travel.
We consider any kind of motorbike or bicycle or truck or some kind of vehicle that allows people to move.
So those are the ten indicators that we consider.
Again, that could be a subject of debate, but are we trying to shield ourselves from that for the time being?
Because this is one option that is already published in international work for international comparisons using different.
So that is for the wellbeing aspect of the work when it comes to the conflict aspect of the work.
Now of course if wellbeing is multidimensional, conflict is again multidimensional.
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
conflict that we want to do to contrast our measure of well-being with and her framework.
I don't know if you know the framework. I'm sure you do.
And if not, perhaps I'm not the best person to explain that. Perhaps you know much more than me.
But you have a very rich framework where she analyses four different aspects or
different aspects of conflict that are changing across time and across space.
So basically you would have different groups.
For example, you have different forms of conflict, you have different means and you have different places.
And of course, you will have different consequences based on data limitations.
We're not able to take into account every single aspect of conflict that she proposes in a very comprehensive framework.
But we're trying to see whether or not we detect differences in terms of the form of conflict.
So we analyse different forms of conflicts, we analyse of course, different places.
And I'm going to get into that in a different in a different slide.
And of course we heavily draw on when it comes to the consequences.
We actually challenge the fact that consequences can be overlooked if we take different strengths,
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.
This is at an exploratory stage. We're trying to draw on equity.
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.
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.
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,
then we just adopted that for the preliminary exercise.
So here you have Nigeria. The darker colours represent higher levels of poverty as measured by our proposal.
So you would have higher intensity and proportion of people who are poor.
So we would take into account not only the proportion of people who are poor, but also how poorly fits the combination of both.
The darker the colour, the poorer the region. So you can see that of course in Nigeria the northern regions are the poorest.
Yeah, and they're also very concentrated in Borno. They're also regions where you have a lot of ongoing conflict here.
The size of the bubble is representative of the fatalities associated to each conflict and each thought is a conflict.
Okay, so here we try to see is what kind of unit of analysis we can actually use for our for our paper.
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.
So we have sample. So data has been collected from every single state and it has it is representative at that level.
We would actually go to have a more granular perspective and a more people centric perspective,
but of course we're not allowed to say what's happening with people.
Yeah, we can only say what's happening on average in a certain region because of data sensitivity, right?
So the unit of analysis can be the region. We know that that's troubling because what border do you do?
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,
but we do have for the sake of data limitations, we do have to respect the level of representative duty of the data.
So our unit of analysis has to be the political boundaries in the region.
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.
This is the evolution of conflict and poverty in Nigeria.
So you can see that over the five years that I present here, poverty has been constantly being elevated in in the northern regions.
It has maintained it has always been lower in the south regions, in Lagos here, for example, and conflict has been ongoing.
Over the regions. You will see that we detect a change in the levels of poverty by 2017.
Yeah, where we have slight movements, but in the end the situation has not changed dramatically.
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
evolutions in terms of welfare or well-being as measured by this this framework that we present.
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.
When it comes to the future, for example, we see a different configuration.
We see that poverty, of course, is much more prevalent in the Somali region.
I mean a far but then. When it comes to detecting changes.
Actually, the whole country has reduced poverty overall. So they have improved even though conflict was ongoing.
Yeah, I mean, you can see that conflict was really prevalent even in in Oromia where it's different.
Of course, it's the least poor region, but it is very prevalent in terms of conflict.
But it has maintained its status as being one of the least poor regions in the country.
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.
They're all very poor. But then you also see that poverty has been reconfigured over the years with dictators.
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.
Yeah. So you can see that here. For example, we see a positive change when it comes to welfare.
In in, in Oriental where there are so many conflicts in the key that we cannot see how it moved, but it actually remains.
So of course, what we can say with this in terms of numbers,
so how do we relate this when it comes to actual significance and differences when it comes to regions?
So I will very briefly explain this because I know that this is an interesting part for a lot, but we basically say,
look, what we can do is we can have a unit of analysis which is the subnational region because of data limitations.
However, we would be very wrong to say that conflict in one region affects people only in that
region or that people in one region is only affected by conflict in that region.
No, because in the end, the whole country is connected by economic systems.
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
can detect whether neighbouring regions are actually affecting conflict occurring in neighbouring regions,
are affecting poverty status in in the region itself outside of the proper direct effect of conflict in that precise region.
So that is what these models sense for.
So here I'm sure that perhaps most of you would recognise this equation where this would be the dependent variable,
which is in turn the intensity, the incidence or the combination of both.
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,
which is T and then s the moment of the conflict, which can be actually before.
So we have a time dependence. So as is any time in any period in time that is lower than or equal.
And then here you have an element that is very special to the spatial balance,
which accounts for not only conflict occurring in Region I, but the vector of conflicts.
Sorry, but the vector of conflict occurring in all the country.
Yeah. So at one point in time, this is why we omits the individual index here.
And then these conflicts are activated through a weighting matrix that is,
and an end by end matrix that activates conflicting neighbouring regions for different points in time.
This is the efficient that captures spatial spill-overs. If this coefficient is significant at a certain level,
then we can see and we can detect whether or not conflicts in neighbouring regions have an effect,
say pure or net of the direct effect of conflict in that position region.
So that is the way we can account for this kind of spatial spill-overs, if you want,
while respecting the unit of analysis, the data mandate, without breaking the physical activity of the data.
So I hope that that's clear. Then I guess that we can go back to that if you want.
And then here, I'm just going to present some preliminary results of. We do have much more results,
but I'm going to focus on a couple that already point to the fact that there is
something to say and we try to distil and be more putting it much more into context.
So here, for example, we focus on conflict side one, conflict side one, our battles according to a clear definition.
And we first don't take into account any kind of spatial, spatial spill-overs, and we consider that only direct effects.
So conflicts happening in one region have an effect on the livelihood of people in that region.
So we are oblivious to the spill-over.
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,
the intensity at which they experience poverty, and a combination of both.
Here you have the countries that are represented in different colours. The North represents the main point, the main estimate.
The lines represent the confidence intervals of that estimate. And if they touch zero, they're not they're not in the cross zero.
They're not significant. And this is on a 95% confidence in the horizontal axis, you have the lack.
So like zero means that we take into account contemporaneous conflicts.
Like one means conflicts occurring the year before data for well-being was observed.
Lack two represents. Two years before that and so on and so forth.
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.
So conflicts occurring in one particular point in time have a very strong, significant effect or have a very strong,
significant association in this case with levels of well-being as measured by the
proportion of people and as measured by the intensity at which they suffer poverty.
But then we also see that, of course, longer term,
longer conflicts are occurring a longer period in time in the past have to have some effect, but to a much lower extent.
What is interesting here, for example, is if you see what happens in Congo.
So if we try to measure well-being by the proportion of people who suffer poverty, then we would reach no conclusion.
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.
But if you see what happens with the intensity at which poor people suffer poverty, it has actually increased.
So that already tells us something about the kind of people who are affected by this particular type of conflict occurring in Congo.
It is not necessarily more people who are going to be falling into poverty.
So it is not people who are at the verge of falling into poverty because they're around the poverty line.
But it is mostly affecting the intensity of people who are already below the poverty line,
are suffering and not only from the contemporaneous conflicts, but also for the ones that occurred in the past.
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.
So we can also see that in the case of Ethiopia, Nigeria, for example, that the effect goes both sides.
So it not it's not only associated to the fact that you have more proportion of people living in poor areas,
but it is also associated to the fact that people who were already poor are suffering from poverty in terms of intensity.
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.
And that's where we try to engage with amidst a rich framework about how to define conflict.
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.
In the case of if you look, for example, we don't expect any kind of effect.
We have a large variation, but we don't detect any kind of effect. And in the case of Nigeria, for example,
we see that this type of conflict has actually been more associated with an increase
of the proportion of people being poor rather than people who were already poor,
being less poor or being being poor in terms of intensity.
So the effect seems to be much more significant in terms of the proportion.
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,
that these for people who are perhaps nearer to the poverty.
Yeah. Now, this is with no specialist groups.
What if we take into account that borders are fuzzy and that conflicts in neighbouring regions can actually affect people living in other regions,
which is likely to be the case? But we are trying to see whether we can capture that with the data that we have.
So here this is a little bit more complicated, but so in colour in.
Yes. That's why I'm showing some results. Because we have many of them.
In rows we have the proportions, the effects or the associations with the proportion of people living in poverty.
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.
And here we have the combination of both. So those are the rows in columns.
You have the countries, you have Congo, you have your period of Nigeria.
The colours here now represent the black. So in this case, green represents contemporaneous conflicts.
This colour orange represents represents conflicts occurring the year before and so on.
Okay. Then you separate what we call the direct effects from the indirect effects.
Yeah. The indirect effects are effects caused or associations caused by not conflict occurring in that region,
by conflict occurring in the neighbouring regions. Right. So in Congo we don't detect any kind of neighbouring effects or spatial spill-overs.
But in the future for example, you do see a short term effect, which is quite significant in terms of indirect effects.
So you see that neighbouring regions in Ethiopia, sorry,
one region in Ethiopia on average tends to suffer from conflict occurring in the neighbouring regions as well.
To a great extent when it comes to contemporaneous conflicts, then the the extent of the indirect effects reduces over time.
So we see a large indirect short term effect of conflict type one that means battles in Ethiopia.
It's interesting to see that in Nigeria, for example, we see a much longer standing.
So we see that conflict in neighbouring regions. Not only affects the welfare state, it's often.
People in other regions contemporaneously, but also we detect very strong, significant indirect effects over time.
So it is a more persisting time of type of indirect effect when it comes to changing, again, the nature of conflict.
And we think of what happens with neighbouring conflicts, but not as measured by battles, but as measured by riots and protests.
Then again, in Congo we don't detect any kind of spatial spill-overs in Ethiopia.
We detect some kind of spatial spill-overs, but again, only contemporaneous, not over time.
And then again, in Nigeria, this type of conflict seems to be very prevalent,
but only in the short term, not necessarily in the long term now, which kind of makes sense.
But we have to dig a little bit more into that and contextualising results that we get.
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.
So one thing that I forgot to mention is so one thing that is important here is that the effects, for example,
the spill-over effect here is as strong in terms of the proportion of people as it is for the intensity of poverty.
So it not only pushes people, say, below the poverty line,
but it also intensifies the extent to which they experience poverty in terms of indirect effects.
Right. So we do see that there are some things that can be said when we think about these welfare aspects,
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.
Or are you confident, precise? And we assess the interconnections.
We're also seeing that and its framework is very relevant in the sense that how you define conflict or what
aspect of conflict you actually think will have an effect on the kind of results that you have to make.
Because if you define some kind of violent protest, for example, a conflict or a battle as conflict,
then you will have different results because there are different forms of conflict, of course.
And then we also see that you can actually say that with existing secondary data.
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.
But what is interesting here and the point that we want to make is that you already have some kind of data available,
which, of course, is not perfect and it has limitations,
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.
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,
is the fact that the link between conflict and intensity of poverty needs to be more carefully assessed because for the time being,
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.
But we don't see the extent to which people who already face this kind of problems have actually improved have actually deteriorated.
Their situation even worse or we don't see the extent to which several aspects of people's lives have been reconfigured and
conflict has simultaneously affected purchasing power and school attendance and flooring and access to drinking water,
etc. Yet we also detect indirect effects.
I guess that this is not necessarily a novelty per se, because we already know that, you know,
conflict does not necessarily conform to political boundaries, not not subnational, even not internationally.
But then what can we say if we only have data that is representative at certain subnational levels?
It's a mistake to try to go beyond that. And that's disrespect to the same time frame, and you cannot do that.
So one way of doing that is by acknowledging that the units that you can define as unit of
analysis that are correct can actually have spill-over effects to their neighbouring regions.
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.
And indeed there are some things that can be said using this kind of frameworks that these are more essential.
Of course, we want to extend this study to other types of conflict and say qualified conflicts, because for the time being,
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.
So we tried to come up with a cleaner, quantifier or qualifier of conflict, for example,
using fatalities or other aspects to try to refine these measure that we have that
for the time being very crude or the actors who perpetrated the the conflict.
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,
but other types of data that if you use, you see the usage, for example, which of course we need to reconstruct.
The way the types of conflict that we can envision because the kind of leader that we have is different.
I think this will see as some kind of entry door or a preliminary analysis to more in-depth analysis.
Because one thing that we do not want to claim in the paper is that this is a generalisation of what happens in,
say, sub-Saharan Africa or in the context. So it is very complex happening because conflict.
Each country and each conflict is fundamentally different structurally, and it has a different history.
So we first want to have a regional perspective, but what we can say and what is the context of each particular country.
But then, of course, that strategic analysis will shed much clearly on the evolution of that country.
And then even if we can, we can see the extent to which this approach can be operationalised through experimental approaches.
But that, again, requires digging very deep into one specific case and not necessarily trying to uncover cross-national differences.
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.
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.
So we try to not only remain of the low hanging fruit of just describing the effort,
we try to see to what extent we can actually link our results to policy and
economic mechanisms that are reconfigured in the period of analysis that we can.
So as I mentioned, this is preliminary research. We are very excited to lead this forward.
And again, thank you for giving us a chance to present this in this platform where we actually meet.
And we will value a lot of comments and feedback from you who are experts much more than perhaps we are in this subject.
So thank you very much.