 Thank you so much. So I'll present the equality comparison for Kenya. That is our equality measures, Genie measures, compared with the U.N. or the U.N. Ewaida. Yes, but before that perhaps I want to just mention, just like a snapshot of how our Kenyan scenario is in terms of equality. And for this we are using our very current kind of survey, three kind of surveys that are nationally representative and of course having used the same kind of method in terms of data collection. Yeah, so one way to note is that using our ACA in quality, that is our diagnostic in quality measure. We note that our in quality trades from 1994 to somewhere 2016 had a mixed kind of a trade. Where we find that initially it was increasing in terms of gene coefficient. But later on in the period 2016-16 it started going down. Yeah, so one thing we need to mention here is that the kind of measurement we are using here is the per capita consumption, compared of course with what we'll be seeing with the WIDI in a moment. Yeah, so we also note that the per malatio was at 2.8 1994 but by 2006, I mean by 2004 and 2006 it was at higher of 2.8 but it went down to 2 in 2016 which is of course a consistent with our gene coefficient. Yeah, so one thing to note here is and of course we think it's quite obvious is that the urban in quality gene was higher than the rural. Of course for the reason that the rural households mostly tends to have like almost a similar living standard. So that is not unexpected. Yeah, so we also note that the asset IDEX remained quite around the same point at 0.54 of our gene. Labour market earning to some extent was higher at almost 0.7 percent but in 1994 but it went down to around 0.59 into 16. Yeah, so something else we note with what we are calling a new constitution in Kenya where our government is in terms of two tyres. In the second tyres you have what we will call the counties. So what we notice is that there's a wide variation of inequality at close to 47 counties and of course which is driven by the fact that there is also a dissimilarity in terms of our development among those countries. One thing we also note with our inequality indices is that it's highly driven by within group inequality rather than between group inequality. So the other issue, the other area that I need to have shown is actually the social domains in terms of education, say health, safe drinking water and also maybe access to sanitation. All those areas of course noted are very mixed kind of traits in terms of inequality where especially in education and health they have shown an increase over time. The same also applies to gender disparity. Yeah, so now turning up that's like a structure, maybe a bit of a landscape in terms of the inequality traits in Kenya. Yeah, so let's now turn up to our UN wider inequality measures. So what we know is that the data, the UN wider inequality database provides data on income in quota globally just like the way we have learned from Monca here and it has two kind of databases. It has what we are calling the original and also the companion with. Yeah, so for the for the weed fashion, current weed fashion, we see that we have up to up to 2019 that is starting from around 2019-14 data points and it covers about two other countries. So we also know that the really database also contains like 20,000 data points and it has over that 7,000 unique country observations. Yeah, so for Kenya the original weed has that two observations and it has a nine companion, weed companion, what we are calling the standardized weed. Yeah, so then the main focus of this analysis as just as also indicated by Monca here is simply to assess the inequality measures of the, if we measure darling Kenya with the comparable period that a weed has because these are measures that are also coming from weed. Yeah, so in that case we say that okay our section here reports the original weed estimates and we estimate for Kenya as reported by UN wider. So what we wrote here is with the original gene coefficient which is computed mainly from income, I mean per capital consumption and most of the most of the data sets are coming from big stain and others. Yeah, so we find that the original gene compared to the gene, the original gene is much higher, I mean the income gene is much higher than our consumption gene as seen from our table there. Apart from this one, apart from this, this is two where we have a very slight difference between which is actually a negative difference and maybe the 1997 which has a zero difference. Otherwise all the other, all the other differences appear to be quite significant when we compare the original gene and the standardized gene for Kenya. So in terms of now comparing the original gene and also the standardized gene, or what we are calling the companion and our ASEA gene, we find again that the difference between for example our ASEA gene and the original gene was quite close. When you look at the percentage difference where we have say B minus A. Yeah, so we find that the difference compared to the difference between our diagnostic gene and our standardized gene is quite high. Remember here our ASEA gene was computed through the income, I mean the per capital consumption while the standardized gene as alluded by Monkahia was computed through the income per capital. Yeah, so then in fact that our looking at the trade, one thing we note is that our income gene as reported by a companion is actually much higher than both the standardized, I mean both the ASEA gene or our diagnostic gene and the original. Yeah, so what we note there is like the trade actually is similar. So meaning that, I have a lot of time, I think I should be done by then. Yeah, so meaning that the issue here could be driven by some scaling which is more like quite something that need actually to be investigated. But it's like a scaling issue there. But the trade, so the style, the companion gene is also giving us the same message. The only issue here is that the message would be the most like we have a higher in quality. So the possible explanation to this, number one is like what Monkah also have indicated that the measure, the way the standardized gene was measured in terms of conversion, Kenya and only some few South African SSS sub-Saharan African countries, only some few are included. So meaning that perhaps the computation, the regression that was used there might not have been very presentive. So we are not very sure maybe that could be an issue there. Then number two, this is the issue of missing information, especially the income where we even tried to do some more computation using the very current data of 2005, 2016, using the income and we faced a lot of challenge when we are trying to use a guide that was used to come up with standardization by the UN wider. So in that case, we think that could also have been an issue that could have caused the kind of difference we are seeing. Yes, so this is what I'm saying, we tried to use that but by the end of the day we are not able to come up with something that was very conclusive. So in conclusion then, we say that one thing is a quality data on income is an issue in Kenya and probably same with most of the African countries so that we are not able to support for now a very consistent measure that would be comparable with our company. So then meaning that this issue of income, income really to be investigated in a further and once you have that, then we can try again to see whether our crystallized gene and what we get is quite might be consistent. So the other issue is we also know that a real companion in quality measures indices, though they are higher than those derived from the per capita consumption and survey and himself data of physical incidence. So what I mean by number B is that we also did, we have an inquiry measure that we have done using physical incidence or what you call CEQ but when you compare the income, the gene between that results and the one we have for the week it's quite high and very close to our gene that we have computed using the consumption per capita. So I think I want to talk about that but I also mentioned here that the other issue need to note is that the weird companion is very consistent in terms of the trading so that we are giving very similar message in terms of the trading but of course different messages in terms of the wilderness of our gene when you compare the two. Thank you so much.