 So I'm Kunal Sen and the project that we started was led by Carlos Gradin, Simone Scotti, Piotr, Piotr Lyodolski, and myself. Carlos is chairing a different session right now, that's why he's not here. But this project was really based a lot on what we're going to hear today in the annual lecture, which is what's happening is that if you look at the US in particular but also some of the European countries, you see in this polarization of jobs and earnings happening over time. And of course the question is why is that happening over time? And in the US it has been quite remarkable with the increasing earnings inequality, the last 20-20 odd years. And the one reason one argument has been put forward is what is called routine bias technological change, which is what you're seeing is that occupations which are involving routine tasks are slowly being automated away. Think of clerical workers, think of factory workers, increasingly seeing automation coming in those areas. While we see less of that happening in the more, in the kind of occupations which need creative boundary skills, the operation of the distribution occupation, and also perhaps even the lower end of the occupation distribution where you don't see modernization happening as much. Think of for example janitors. So that particular phenomenon that we observed in the US, the question was that like do we really see that happening in developing countries? And that's an important question because clearly routine bias technological change has very strong implications for inequality, right? And of a different kind of implication than the earlier literature we had on skill bias technological change. So there was a different implication which we saw in the US. And so what we decided to do in this project is we decided to first understand the implications of routine bias technological change in a range of countries in Latin America, Africa, Asia, Middle East and Southern Africa, and also South East Asia, and try to make sure that we do a comparative analysis. So we were very, very clear, and Simone was very much involved in that, to use similar measures, occupational tasks, thinking about also economic, economic methods, descriptive methods, so that we can kind of see, okay, from all the evidence we've got, we can say something about what we see happening in Latin America versus Africa versus Asia. Both across space, but also over time, because we see and I saw the papers we see today, we see big changes happening in inequality in these countries for various reasons. So the core question is that how important is routine bias technological change versus other drivers in inequality, earnest inequality. The skilled premier institutions, political institutions, which is what Mohamed spoke about a little bit earlier in this conference, so how can we kind of isolate the effect of our routine bias technological change from the other drivers of earnest inequality. So we have three papers here in this session. We have a first paper which looks at global, the global distribution of tasks, sorry? And? And Ghana, sorry, yeah. Oh, sorry, you're talking about Ghana. All right, I got that wrong. So we have three country case studies then, okay, three country case studies, one on Ghana by Simone, one on Brazil from Sergio and one from Argentina on Apollo, right? So we have three case studies. So yeah, two Latin American, one African. And I think you'll see what you'll see in this case is this attempt to try to use similar methods and similar thinking about routine bias technological change. So hopefully at the end of the session you'll have a sense of also the big picture issues are coming out, which I think also if you regret to do that to all the presenters, speak a little bit of the big picture question coming out from your presentations. All right, thanks.