 Thank you. Thank you so much. It's a great pleasure to be with you and to talk a little bit about ASA and to integrate what ASA does in what you've heard this afternoon, this morning. Sorry I'll get it right. And in a sense the presentation is reflected ASA's style, which is to actually get stuck in and do the hard work and try and do something to build a base for understanding African inequality in a very careful way and then talk about what we can say about African inequality but not shy away from the hard work, but then also not shy away then from the ambition of trying to explain that inequality. So that hasn't been a strong feature of our presentation this afternoon, but you need the basis of good data to do that. And I guess ASA's it's been around since 2018 and its hallmark has been to build these data sets and to build some tools. So what you've seen when people spoke about the ASA perspective on inequality and its trends in each of the three country contexts, those were built in partnership with the national statistical offices in each of those three countries. In fact the South African report is the first South African official release on inequality ever. Well I don't know about ever, but as the record somebody, some historian might find some clay tablet somewhere, but that close working relationship on the data and the data quality and into the policy space, into the SDG measurement space. So we've had some impact on what the countries add to their inequality reporting on SDG 10. Given the texture of the inequality it makes sense for some countries to be tracking lots of things and you've seen we don't just do income and expenditure, we do multi dimensional inequality and then the art of the work program is okay but how do those multi dimensional inequality intersect to reproduce and to produce the levels of inequality that we see and there's a particular focus in ASA actually on the dynamics of inequality between cross-sectional time periods because we have two very very good panel data sets in the continent, the Ghanaian socioeconomic panel and in South Africa the national income dynamics study panel which have to be the two best panel surveys on the continent and then we've also accompanied that work with a series of the commitment to equity, the benefit incidents, exercises, we've done them in each of our contexts, we've done them with the ministries of finance or the national statistical office as the bridge then into the policy discussion with something to say and in order to stimulate the national dialogue and then we've worked quite hard to try and increase the accessibility of the data for the national discussions of inequality to take hold and to stimulate the national discussions. So as you can tell there's a lot of sweat in there but it's that that's the way we've chosen to go. Of course we want a key foundation, founding principle of ASA really is that there's a lot of there's a lot of discussion about African inequality and it's growing importance in on the global stage and African poverty as well but a lot of that discussion doesn't actually embed itself in in Africa with African researchers and with an interface into the African policymaking fraternity and so we do have the aspirations to be talking about African inequality in general as well. We just started with our core basis and this is a key point actually if you look at the graph that I've got here these are measures of African inequality on the continent but by country that are produced one by the World Bank and you can see what came up before it was on Moon's slide was the extreme inequality in Southern Africa it's by miles the highest inequality region in the world and in fact if you take that art or just for it in Africa's inequality trends then Africa falls from being right up there with Latin America to the middle that doesn't mean that's the accurate characterization it just means you've got to be careful there's heterogeneities across the continent that are crucial. There's another picture here that's derived from the World Inequality Database in 2019 that doesn't just use the Living Standards Measurement Surveys of the World Bank but uses a combination of those surveys and fiscal data and national accounts to produce a picture of African inequality and it doesn't I guess take my word for it you can look at the PowerPoint later. They don't map exactly that they're roughly the same but there are many differences and in particular notice the fact that at the top end of the continent or their pockets on the continent where there's no survey data but there are estimates from from there but these are the databases that are being pushed into the big discussions in the UN around about African inequality and what we need to do to tackle it and so these differences are very consequential and it's not very very comforting actually to come back to my point about grounding inequality in in the in the policy processes but also in the dialogues within countries and then across the continent it it's not appropriate actually that the that these high level discussions are almost absent of Africans African voices and the data differences are consequential too so who's talking in what forum about African inequality and we just we don't just want to do what I'm doing now do missionary work but we were actually want to grind out then okay we need to come with something to say into that space and you've seen a bit of that today but not much about the multi-dimensional work that we've done right and so I just reproduced a slide from the South African presentation that that shows the paper that we've written recently using demographic and health survey data actually and some asset measures that we have developed ourselves to measure asset inequality and that's just to make the point that the objective of the exercise is to explain inequality and to to be able to focus strategies to overcome inequality okay there's a lot of missionary work but I think that's okay given all the data stuff we've had. I just want to make my point by little advertorial to a session this afternoon where we'll be talking about some work that we've done using the the WID data just to say it's possible it's not that we don't have the skills to use these data sets but we want to ask ourselves the question should we be using these data sets are they telling us stuff that's useful so we did use the WID data in this case and this is this is the famous re revisited elephant diagram updated a little bit where where the elephant went on a diet or something lost lost the middle and what we did was add Africa to that and it's and it looks impressive right and it is impressive and and it's important too if it's if it's true right and we're assuming it's true and we're just making a point here a very important point where does Africa sit in this world picture when you pull all the data and you do the world distribution well you can see these incredibly high shares of of Africa in the in the bottom percentiles of the distribution and it helps you well we'll tell you why that's important this afternoon but for now I just wanted to make the point that one of the reasons we're getting into this these data quality issues is so that when we come into the international discussion we're the respected voice that's what we want to be and you've got to earn that she has another this is about the African inequality by percentiles and it just shows a very high top end in fact the top end jumped so steeply it disappears off your slide right at the 99.9 percentile okay so how many how am I doing thank you you give me a lot of latitude here that's dangerous anyway so let's wrap that back that's about ASA but it helps I think ground the detailed work so we started with the hard work and then we're coming back out again now to tell you to try and show you well why is this important because we developed these country diagnostics of inequality the the there are a number of international comparison comparable data sets the world I showed you one from the World Bank showed you know one from from the the the world inequality database and then obviously the weird one that's been around forever for a long long time particularly designed to get the trends right and if you think about the work on on monitoring STG progress and things like that the levels are important of course in terms of the texture and what you know and and and and then the texture of inequality is important too but for the STG monitoring process you got to get the trends right in whatever you're measuring and so it's a particularly unique space I think for the with data in these national discussions is just focus is so hard on the trends okay some key points that came out across our country presentations so this is a very fruitful dialogue I guess that's what I'm trying to say we really do appreciate the the joint project with you and you wider on this and just to raise some of the issues that come out in the comparison well obviously there's a focus on income inequality and it turns out the South African income data is is is there the the the other in Kenya and Ghana the income data doesn't look particularly good and it's a bit of a puzzle if you think back to the Latin American discussions that we've been having right the Latin American discussion is income and they've even got harmonized income across the whole continent and it's unleashed an incredible amount of really good research right and and it goes to some policy issues you do need income data you might not only need income data but you need it anyway so so the income that's one of the issues and the comparison of of income versus consumption and I guess one of the the key wins the one of the reasons we wanted this project actually is the fact that Africans produce consumption data mostly driven because the focus has been on poverty reduction right in the continent it it's not obvious there are measurement issues with consumption data too but nonetheless this mapping between income and consumption data for which is important and I guess what we're getting out of our country studies is that is that the the weird data is combining income and consumption and actually earning sometimes and when we bring our multi-dimensional perspective to that we can actually help a lot to the series it's it's not clear that income and consumption should be put together rather than the relationship explained in any country context they're not necessarily exactly the same concept but what what are we supposed to do I don't think in the South African context we saw no major violence is done in in in Ghana and in Kenya it's not so much a question of violence the South African context the data is used in the wood series in the Ghana and in Kenyan context it's that they're adjusted for Cote d'Ivoire actually it drives a lot of what you see and the gap between income and consumption etc. becomes very important so so just interrogating these concepts that's what you get out of these country studies and it's I think it's incredibly important actually so and that's the punchline really I think I think that's bringing the whole analysis of inequality to bear on on the weird data is is the way to triangulate how are we going to decide at the end of the day about what's going on in Ghana other than bringing our full understanding of what we've got consumption income for sure but the other multi-dimensional measures also count and help in the validation okay I'm gonna am I now I think right okay so I'm not gonna get us gave you the high level on the data I think there are some some lessons I told you about the income versus consumption and and the fact that you know it does seem that we need to think very very hard I think talk when he supported quite nicely actually that we really do we need income data but we need to think really really hard about how to measure it was a bit of a puzzle to me why why we don't seem to be doing very well to some extent I think it's because we haven't taken it very seriously across the continent actually that's my that's one of my views so coming into this we get the the word and and I think you know to 20,000 observations maybe that's too many but these country studies then can really help that the wood ridge companion reduces us to one preferred estimate per country per year so that's then a very nice vantage point to get away from the 20,000 to interrogating that preferred estimate and obviously I think the the the different countries give different value add in comparing the estimate in any given year so in South Africa we do have multiple estimates of the same year we've got a lot to bring to that that comparison in the South African context and it is part of the the harmonized wood series in in Ghana and Kenya we don't have that but we can what we can do then is is bring a lot of value add in terms of the type of standardization and smoothing that is used in the wood data for African context I think that's what we've got to bring so I said all of that luckily for you guys it's a talk when he's a showed us the slide and I just had one key point to make from that it in some senses that the wood data needs to pick linked link series if you're gonna if you're gonna do trends and changes over time you need to pick like a base here in a CPI almost right you got to pick a year to link the series and what one of the things the South African as example makes clear is that actually the choice of the anchor for the link is really important if we picked at 2015 instead of 2010 we we can produce a slightly different graph but in general the South African case fits pretty well and that's not an accident it's involved in the thing but it's still a sophisticated relationship okay so we've got work to be done as part of the project we're wrapping it up now through to the end of the year and really wrapping it up in a way that tries to integrate the narrative of inequality in each of our country context and then one paper that really looks at the cross-country African work that's done out of that and these are all key issues for Asia and I just end with a little inspirational message I think this is absolutely crucial work I don't know how inspirational it is you need a nice picture or something but you know it's just to end where I started the discussion understanding African inequalities and what can be done about them and why they're so important in terms of how they stifle countries at the national level but citizens within countries as well it's it's such a crucial agenda actually and and you know the data are not great but they're not there they are data they just have to be used appropriately to tell the story thank you