 So now, if you look at this title, it's on incomes, inequality and poverty. Actually, just by chance, usually we think of poverty as being explained by inequality and income. And Eric and Ali, in the early days of the late 90s, they actually analyzed the correlation between poverty and inequality in Africa. And in that relationship, actually, inequality and poverty are strongly, positively correlated. So actually, if inequality is rising, you can expect that poverty is also rising. And even incomes are growing. Also, poverty is actually going down. I also can't help noticing that poverty also affects incomes and inequality. So actually, analyzing that relationship is quite a complex task. But this is not what we are going to do. This is not what we are doing here. What we are doing here is we are taking income, inequality and poverty as in different aspects of well-being. And we are trying to see how they have evolved over time. Actually, over a long time, over a period of about 100 years, and this 100 years, actually from about 1900 to 2012. That's a period that we are looking at. And this period is long enough we chose it that way to permit identification of persistent determinants of poverty. And also, to also try and see whether you can identify potential sources of structural change, which actually drive both incomes inequality and also poverty. And in the analysis, we focus much more on the recent period for which we have more comprehensive data. And just some advertisement down here. We depart from previous literature in two respects. One, we actually use several data sets rather than just one data set. And we use several methods to compute poverty line, which is key actually in estimation of poverty. We use the utility consistent method that we actually taught by UNWinder. There was a conference for us to learn that method. But we recognize that money metric is not enough to measure poverty. So we also use money metrics. And the motivation, as I will point out later, is actually the market failure argument. If the markets are working, the only thing that we need is income. So just beginning our long-term analysis. Now there were dramatic changes in Kenya during the 20th century, from the 1900s as I said, to 2012. And during that time, there were shifts in the structure of output. So the share of agriculture moved from around 75%. The share of agriculture in GDP moved from 75% around 1900 to 25% basically. And so there was actually, there's a structural transformation in the economy over this period, which still continues to drive incomes, income distribution, and also poverty. And later on, I will say more about structural change. Now, so we want to focus on this period, okay, 1914 and those 15 years. This is actually when the British began to exercise their control of the Kenyan economy. And during that period, the Kenyan inland was not integrated with the outside world. And actually even the groups in Kenya also themselves at that time were not integrated. But one thing which we noticed is that the standard of living throughout the country in various regions were comparable. Very little differences between, across regions. And actually the equalizing factor was the abundance of land. Land was abundant, so everyone actually would get something, get a decent livelihood as of that time. But actually land scarcity now is a dividing factor. So you can see one factor now can play different roles over different time periods. Then another event, which happened during this time, was the completion of the railway from Mombasa to Kisumu and Uganda. And there's also a major impact in opening up the countryside for settlements, both by the natives who were there at the time, the colonialists who came, and people who migrated to come and look for new opportunities there. And this is the next period, this is still the same period. As the inland continued to be such a long-run form of sector employment emerged, and a class also of traders also emerged. So there were an answer there and also business people, and then began production of cash crops. And so actually the economy began to be modernized. And at that time some inequality now began to emerge in a number which were not there before. So actually this period was as small as the origin of the inequalities that we see today. And actually this is the point that we are making in this slide. Then there's this interwar period. So this is the interwar period. The main thing to notice here is that the African farmers began expanding their production also to take opportunity of export markets. Now this is the period just up to the period of independence in 1963, this one we got independence. So during this period there was extensive winch employment in the country, but actually it was of a temporary nature. People would come to the urban areas, work for a short time, and then go back because of the restrictions. And there was also the beginning of rural urban migration, which is also a source of structural change, creating distinct rural and urban sectors in the economy. So this was the main events there. And during this period, this is now the first independence period, 1960 to 1976. Now during this period there was the change in the interrelation distribution of both power and incomes. So actually the incomes and assets were in the hands of the colonial setbacks. Some of those assets passed into the hands of the natives. And that also started to increase the inequality. But what happened actually during this period was a huge increase in winch employment, especially in the public sector, and the winch increased by 48% compared to the 6% increase in the same winch in the private sector. And here, during this period, a group of co-poor people, the people who were extremely poor also managed, and that is still a problem today. And these people actually were characterized by having very little land to cultivate on, and also very low earnings from their off-farm employment. Many of them were also under arrest. Here, this shows that this is around 1914, this is up to 1976. Sorry, there is some information missing here. This is the trend in sector income share. So this is the share of winch in total incomes. As you can see, this is more or less a term of independence. Roads are then declined. And then this is now the share of agricultural sector in total GDP, total income actually declined substantially. And this is the share of profits, also rose and then declined. These trends have implications for poverty and income distribution. And this is the share of income by race. This is the share of total income by the African population. Actually, you can see it's the highest because they were the majority also. That's the reason. And this other group here, this is the European, not very different from the Asian. So, in terms of the evolution of poverty and income from 1900 to 1976, what we find is inequality actually increased until the 1950s, then fluctuated. And we see it from here, this is gaining coefficient. This is for everyone, this one here, actually increased. Then it kind of fell so still high that the inequality. And then this is now for the inequality in the modern sector, it was the highest, simply because of the way we mentioned it using income from the former sector. And the inequality among the Europeans was actually the lowest, but also actually for some reason it fell after independence. Around this period you can see it fell from here. And this is an important graph for us. This is the evolution of poverty over this period from 1900 to 1976. There was no FGT at this time, actually. The FGT method discovered by Eric cannot discover this method at the time, but of course there was the anti-country issue which was there. So this is the same index which is modified by the inequality among the poor. So the point to note here is that actually poverty has been quite a problem in the country for quite some time. And then after this it stabilized, 1976. If we can use the data sets for 1982, 1980s, 1990s and so on, this would just be around there. So actually around the forties there. But it has come down, but it's not as serious as it was before. Right now we think we have a lot of poverty, but poverty used to be much a bigger problem if you say that now. And this is a more recent period and we are trying to see things that might actually be in driving the incomes that we measure. Now this is one factor in driving that income. This is the capital rubber ratio. Capital rubber ratio is this one, which is rose and then declined. And this one here is, yes, this is the land delivery issue. Land per person declining very sharply actually. Okay, and this is also one factor explaining poverty. And this other one is the capital rubber ratio, the capital land ratio actually. So capital has been accumulating faster than land. Of course land doesn't grow, but capital has been growing, so that's why you have that relationship there. But because of this pattern here, this one here, okay, you can see the return to land is actually very large. Okay, so now other people actually, land is an issue in Kenya now because I've become very short and very scarce, and there is a high return on land. Then for GDP for a more recent period, here this line here is the rubber land ratio. Person per land actually, this has been rising. But you see the slope here is very slow, but actually if you look at the over the wrong period in the other picture, because I don't have much time, I won't go back. Actually the problem is more serious than we see. And this is the labor income shares. These have been fairly constant actually, the share between labor and the other factors of production. Okay, not changing much over this recent period. And so we have monetary measures of poverty, which we look at and these are insufficient health, poor health, nutrition, it is just, actually gave us an idea about this, insecurity, low self-esteem and powerlessness. These are the measures of well-being. What we notice is that all the status in Kenya has been improving. If we look at, if we use mortality, but if we use life expectancy, it's actually uneven. Okay, but this one is fast-moving indicator, this is slow-moving indicator. And so we want to say something about policy with the results that we have. So in Kenya, the aim of our government now is to reduce policy to reduce poverty. Okay, so we actually, and this policy will succeed if certain things happen, if certain objectives are achieved. It's even growth actually occurs. Okay, but growth in itself will not actually reduce poverty. It needs to be, the process needs to be inclusive. And it showed us an example where Kenya actually reasonably well, but poverty did not change much, did not fall much. And we also need to change our political governance systems and reduce poverty and increase efficiency in the use of the resources that we have. The other thing that comes out from our long-term analysis is regional inequality. If we reduce inequality across regions, actually we will reduce overall inequality. Okay, so if we can focus on this thing here, these two are very closely linked. And the issue now is how to transform, to do this we need to transform our economy. And this is why we ask actually what do we mean by this structural change that we want. And I'm trying to think about it that in my mind as to what structural change is, keeps on changing. So what are the measures of structural change? Factor proportions, right? Capital labor issue. Okay, if that we see that changing, then there's a structural change. And also the other thing is the change in the age composition of the population. Okay, now what might be the changes which what factors might cause factor proportions change? Technical knowledge. Okay, norms, actually for me, these technical knowledge, norms and institutions and the mechanisms for social sharing, these are the issues, these are the structural changes. Okay, and if we can figure out how to change the norms, how to increase our technical knowledge, how to change our sharing, because this will affect inequality, then we can actually change both the inequality and poverty the way we want. Okay, so my time is up. Thank you very much. Thank you very much.