 So, I'm presenting the paper on behalf of my co-authors, Boniface, Epo, and Frances Mbaye. And actually, just to say that this, our paper, and actually the project, give you out of a conference, we're ending you a new winder, invited by our chair. Thank you. Continue. Yeah. So, that's the backing ground of the plan of the presentation. And so, to achieve sustainable development in Africa, reforms are needed, okay, which will reduce inequalities of opportunities. These are exogenous sources of well-being. And also, reforms are needed to initiate and sustain inclusive growth. So, inclusive growth is pro-pro-ungrowth that benefits the 40 percent of the population in absolute and relative terms. Reforms are also required to initiate and sustain pro-growth poverty induction. Actually, this is what the paper by UC and the topic as actually is their main finding. So, this is from them. So, pro-growth poverty induction is poverty induction that enables the poor to increase their own incomes via participation in the growth process. It's not difficult actually to realize that the poor can be only low cost of the growth process. Here, we actually pro-pro-ungrowth makes it possible for the poor just to stand by as growth occurs, and then they get some distribution from that. And the two approaches complement each other, okay. And so, but in order to start this process, where in growth it induces poverty, and poverty induction also leads to faster growth. Now, policy makers need evidence that will enable them to fill out how to increase the incomes of the households. And also to fill out what happens actually between equality when it is distributed policies are implemented. We need evidence on that. Now, on the first point, how the incomes of the households can be increased, we use econometric analysis to show actually the role of human capital formation in increasing the level of the world being of the population. And on the second point, how the distributive measures affect income inequality. We use counter-factual simulations to generate that evidence. Actually, you have handed some of this in our planning session. So, actually the main objectives of the standard are two, the standard effects of human capital on household well-being, and we process the household well-being by capital and not equivalent household consumption. And we assess or want to assess the impact of circumstances and therefore on inequality in household income, actually, which is our approach for household well-being. So the literature, we have learned a hand about it, so the contributors of literature in this room, so I will not actually dwell on that. But on concepts, I will clarify a few things. So there are too many determinants of well-being of the household. How the household is doing economically. The first set of factors are the 40 factors, and the key one is human capital, and we focus on health and education, also employment to some extent. The circumstances which the household cannot do much about, given the narratives, this is land, the infrastructure, where they live in gender, and so we actually also show this in our planization. And now, how do we investigate the effect of circumstances and effort on well-being, on the per capita income of the households? So the first question there, okay, we are regressing the well-being per capita income on circumstances and also on effort. But if we do that, we have an endogenetic problem which I will not say much about, and so what we do is we estimate equation two in a stand, where we actually get, where we endogenize effort, using some instrument that we, I may discuss later, if we have time. So actually, so we use equation number two to do two things, one, to find out how circumstances affect the level of income, and also, we also use the same one, having estimated it, to do counter-factual analysis to ask, suppose we equalize circumstances, we give everyone the same level of land, amount of land, leaving effort the same, what happens to distribution, and we do something similar with effort, and we use this method given to us by Wound Range. So the data is from, actually from Cameroon and Kenya, for Cameroon, the data comes from the Ausund Survey for 2007 and 2014, for Kenya, 2005 and 2015. These data sets are similar in the way they are collected, actually. So we, so they are measured basically the same way. So the key result is human capital formation and the circumstances both affect the level of the Ausund Wound Range, and also the distribution of that Wound Range, of that per capita income across Ausunds, but in complex ways. Okay, they vary by, by type, by type of say, level of education, the quality of it, and also they affect the volume by region. And we find that the same, the effects of circumstances, and also efforts actually, sorry, the effects of circumstances are also as complex. So we start now, this step one presents the, how effort, human capital in this case, years of schooling, affect per capita income. Actually you can see the last column there, the effect is positive. So people with the schooling, a year of schooling, in this case for Cameroon, increases per capita income by about 3%, 3.4%. And we also emphasize that the omitted variables which interact with the schooling, which affect also, which affect the per capita income, and those omitted variables actually tend to reduce the effect of the year of schooling. Okay, so that's actually the main point in this table. But then the circumstance variables here, which are age, in this case, actually the effect is U-shaped and female-handed Ausunds. Contrary to what we might think of higher income study than male-handed Ausunds. What problems to do with the fact that females who are hand of Ausunds have other attributes that we don't see, like they may be better educated, they might have other attributes that contribute positive towards income. Okay, so that's the main point there. Now we come to Kenya. What we find in Kenya, the main human capital variable here is sickness. What we find is people report sickness have much lower incomes than people not reporting sickness. Assuming they're not sick. Okay, actually that's a very, very huge negative effect on income. And it's not surprising even that we are talking mainly about agricultural Ausunds where sickness can basically wipe out a family's income. And again, there are omitted variables in this model. We tend to interact with the sickness to increase household incomes. Okay, that doesn't sound intuitive, but we can discuss those. And circumstances variables also affect the same income the same way as we saw in Cameroon. The end effect is U-shaped and now the shocks, the root basically drought and so on, have negative effects on household income. Now, this table now here, we look at the inequality in the actual distribution. In the actual distribution of well-being with this income. Now, some people may ask, why are we saying actual? And actually these are estimated. Yeah, these are estimated relationships. It's simply because of the property that these linear models, the means, say the main income in the predicted income is the same as what we observe in the data. Okay, so for overall genie, for Cameroon, between 2007 and 2014, the genie actually increased for overall, increased for overall. But for Cameroon actually, it decreased instantly. And you see something similar for genie return to circumstances and genie return to effort. And if you compare now the action under counterfactual changes in genie, in the first column there, what we see is the overall genie there for actually 0.4, actually 41%, when now we equalize circumstances, it drops to at 6%. So actually what we see across the board, across the sections, is equalizing of circumstances actually induces inequality. So circumstances are making sure that people have the same opportunities, isn't going to equal it in society. And we find something similar, actually in Kenya, but looks the opposite, but actually it's the same effect. So if we equalize the circumstances, yeah, circumstances, yeah, there are shocks, those are lack of rain. So the overall, what we find is the overall inequality in genie there is 0.39, it's actually increasing. And if you also equalize sickness, what you find also, the inequality increases, other means rising. Okay, so actually, if you equalize shocks, bad things, you can equalize sickness, this is also a bad thing. The effect of that is to increase inequality. If you reduce, so okay, sorry, the opposite of course, actually this results showed, this table shows the same finding as in Cameroon. Okay, so policy messages, so equalizing of 40 written variables, for example, education and the health is inequality reducing. Equalizing negative shocks, for example, livelihood risks due to pandemics and the crop failures is inequality increasing. So on this point number two, if we increase, say, if we equalize better health, that actually, that reduces inequality, which is what we see in Cameroon. Not really equalizing human capital endowment, endowment, is associated with increasing growth and also is poverty reducing. And the last two points here are that there are circumstances like weather shocks and pandemics that we do not want to equalize. And also there are many tests that we don't want to withdraw from population, that is already benefiting them. So in that case, we need to figure out how to bring more amenities to the people who don't have them rather than equalize. So the time equalization depends on the context, terms of the effects that we want to get from equalization. And these effects of policies, it depends on how they are done. Thank you very much, Mr. Chairman.