 Hi, my name is Malo Kalei Namivazo and I am a research fellow here at UNIRO weather. I will present a paper which was done within the framework of the growth and poverty project or the GAP project. Here I am doing a multi-dimension of the dominance analysis for the Democratic Republic of Congo. What is the objective of this study? It is very simple. It is just to have a welfare ranking of the 11 provinces of the DRC based on the children's well-being. And for this we have selected two samples, the sample of 0 to 6 and sample to 7 to 70. And for this we are following the first order of a dominance approach developed by Channing and all in the paper that was published in 2012. We have used the data from the standard demographic and health survey. As we know, the DRC is considered a fragile state and for the last two decades it has been in a civil war. But following the cessation of the war since 2011, there have been signs of work gathering. Now for the last decade, the DRC has a 6% average economic growth and there are also signs of recovery in the agricultural sector, the industry manufacturing and survey sector. They are all back to the pre-level before the war. And unfortunately, this economic growth did not translate into ameliorating the loss of the people in the DRC because the DRC has the lowest ranking based on the human development index. And there is at least 52% of the population living with less, less, less, less than $2 a day. For the method, the first step is to select the profession indicator. So for this, we have followed the profession indicator, but we selected a different profession indicator for the two samples. As you can see, for the children from 7 to 17, we have water, sanitation, shelter, education, and health, which are basically captured in terms of one, if the child doesn't have access to an approved lactation for sanitation and zero, for when the child has access to approved sanitation. For the children from 0 to 6, we have looked at the basic needs for them, again, based on the Bristol indicator. And for them, we have chosen the water, sanitation, shelter, health, and food, the profession. The food, the profession is a composite index of standardness with a resting on the weight. We have chosen to put it as an index because only 4,000 of these children were selected for the ultra-primary measure. So we could not have a really high share of these children from 0 to 6, that we had to divide the sample size. So from that, we did a static, and then a boot, a boot, a boot, a bootstrap comparison. The static is basically a one-time comparison, whereas the bootstrap is 100, it is 100. It is a comparison between the provinces. Therefore, we also have different ways to interpret them. The bootstrap is dynamic, it's more dynamic than the static. Those here, as you can see, those are the probabilities that one provinces on the road dominates the column. And those are the average of their dominates. The higher is your road average means that you are dominating more provinces, and the higher road column means that you are being dominated by the other provinces. And here we have it in terms of probability. The one here means 100% domination, comparing to here just means a one-time domination. So looking at table one and two, we can see that the provinces of Kinshasa, which is also the Krak capital city, dominates almost all of the provinces, and based on that, we classify it as being a better hope. The second best performer is Krakatanga, which is a province in the southern tips of Congo. He is a mineral provinces. And looking at the column, you can associate oriental provinces with the highest column dominates the provinces. It means that he is the worst performer. And we have further proved this by taking the next dominates, which is the difference between the row and the column. The reds, the dominates, means they are negative, and among the reds, you can also see that still the oriental provinces has the highest, it means that he is the worst performer or the worst of provinces. And going in, in, in, in, in, in the table for the bootstrap, we have similar results but the, the, the advantages of this bootstrap is that now we, we know at, at, at, at least that, so for instance, the oriental provinces, which is again the worst performer, but here we can say that Sudkivu dominates the oriental provinces 55 percent of, of time. We were raised here, you cannot say that here because this is under 10, under 10 minutes. And the same also has, has been done for the, the two, between 0 to 6 on, on, fortunately due to the lower share of, of, of sample that has, has been taken from the data, we cannot, we, we could not have a, both the static and bootstrap have given the same results. Now I, I would like to, to get your, your attention to those number in, in, in gray. Those are the, the, the number that gives us the inequality. Assuming that all of the provinces are equal and this is, this is the, the, as the average of the row and column, if all of the provinces are equal, this number should, should be zero. That's because now any, any, any, any, any, any changes either from positive or negative of zero, we can take it as a measure of inequality. So from looking at 0.121 from the static case to the 0.20, we can say that there is a decrease of inequality among the 11 provinces only for the two, two children from 7 to 17, but not from children to 0 to 0 to, and we have, I have interpreted this because a further the, the segregation of, of this result shows that all of the two, the two zero and six are similar. It doesn't really matter where they are, they all have the same characteristics and have the same professional indicator. So, the pregnancy impacts for this study is, is basically the result shows us that there is a local wizard located to king, king, king beginning to exercise and not in, in Snap to the region. Therefore there is a need of real location of resources from king to king to the the oriental provinces and the other provinces. Thank you.