 I go straight to what we are doing, do remittances improve income inequality? That's the basic question that I and my colleagues in the Federal University in Dufodil, Nigeria, decided to undertake and then to answer based on this data set. It's a triple-trong project that we want to do in Nigeria, Kenya and Senegal, but this is the first part of it. So the basic question is, do remittances improve income inequality? And because of the popular problems that we have regarding remittances and migration, we are going to try our best to solve the problem of endogeneity and also the problem of the fact that the impact of remittances might not be the same throughout the income levels. So in the form of introduction, we are aware about how much remittances and migration have generated a lot of research interest. And this is basically because of the volume of flows coming from the developed countries to the developing countries. A lot of people have described this as another form of counter-cyclical insurance. So the basic question is, does remittances, do remittances affect growth? And questions, these questions have been answered, but there are various channels through which this has happened. It could be through enhancing financial development, and Agawa has done a lot of work in this area. Human capital development, just yesterday, we had a presentation that informed us that remittances are actually increasing human capital investment. Also, it could also be through increasing the level of investment in the receiving countries. LATI did a lot of work in this. And the basic suggestion was that remittances alleviate the credit constraints that firms face. And it's also part of the reasoning that remittances reduces macroeconomic volatility. And Rata 27 was very popular in this by saying that remittances is actually counter-cyclical. So which means basically that when economy is going through downturns, migrants tends to send more than they used to. So my motivation is basically this graph. And how much remittances has moved in Senegal? You see, from 1992 to about 1998, it's basically less than $100 million. And this is just about 0.2% of Senegal. I mean, the regional, South African regional GDP. But starting from 2009, see how phenomenal the growth has become. Obviously, we can't ignore how much this is impacting on the recipient countries. Oh, sorry. So this work that I've been doing in this area has tends to say that remittances improve economic growth. It improves, I mean, it reduces poverty both in terms of its incidents and in terms of its severity. But one question has not been addressed properly. That is how much this is affecting income distribution. That has not, especially in our case study of South African and in Senegal in particular, this has not been answered. And it's possible that remittances might improve, I mean, reduce poverty. But we can't take it for granted that since it reduces poverty, it has also reduced income inequality. Because generally, all the research work has shown that remittances is productive. I beg to differ within the IAEA. It's very productive both in terms of human capital development, both in terms of self-employment and even in labor supply most times. But it might also be affecting inequality. It might also be affecting income distribution. Basically because there is a selection going on. I mean, there are certain groups of income earners that might be more likely to receive these remittances. And there is a big debate about whether it's the lower rank of the income distribution or the top rank of the income distribution. And we'll come to talk about this theory, how they try to predict how this could happen. Based on this, our method should try to solve the problem of endogenizing or selection bias. We try to measure impact of remittances on income inequality using the migration server. The main motivation for the research is that the idea that advanced distributional impact of remittances documented by a section of the literature might be because of the positive selection going on. So I better explain what I mean by positive selection. In this diagram, I try to divide contribution in this area into two. And one is that, I mean, this is a lone voice in this area as long as my search is concerned. Bodger 1987 tried to say that the lower rank of the society are more likely to receive remittances. This could be as a result of altruism. It could also be as a result of opportunity cost. Because these are any less, these lower household families, lower income household families are really any less in their country. So if they migrate, they have nothing, a lot to lose. So that might be what Buhay is saying. But let's think about how the remittances they receive as a result of migration might be affecting their productivity, affecting their welfare. One is that remittances have been shown to increase consumption and investment as well. So in that case, we can be saying that remittances increases the welfare of the poor households who we assume in this segment are more likely to receive remittances. But if this happens, what does it do to inequality? You see that at least because of its impact on investment by the households, we should assume that it's using inequality. Inequality goes down because the lower income households are more likely to receive remittances. And they are also likely to spend it in investments which will increase their future earnings. But what if we go about the dependency theory that once these households receive remittances like what India has said, they tend to reduce their labor supply and then depend on these remittances. So in the long run, it's really a fact that they will become poor. And then what it leads to is more inequality in the society. But then there is a bigger voice on the positive selection. There are a lot of words that try to say that the upper class households are more likely to migrate and they are also more likely to receive remittances. And this is because of the cost of migrating and also other requirements of the host countries that these upper class households are more likely to meet. So if that happens, the top class families are more likely to receive remittances and then they are going to become richer as a result of receiving this and then inequality also goes up. So this is what theory, I mean literature, has had to say, I keep going backwards. So my method, like I said, will try to solve two problems. One is endogeneity and the second is the fact that remittances might not impact uniformly along the income distribution. So what do I do to solve this problem? In the first instance, I have to solve the problem using instruments like other presenters and other research has done. But far more than that, we will try to also capture the fact that remittances might not have uniform impact. So that's why we have to choose instrumental variable quanta regression. And this was suggested by Chokuhubu and Hansen that has been applied for the measurement of wage dispassion by Haddon and Lamaqit. Banget has done this in Kenya, especially with remittance data. But so we chose instruments that we think will be able to generate a random selection into migration and remittances. And the two instruments that we have chosen is the ownership of non-agricultural land which reflects wealth, wealth ownership by the migrant-sending households. And we decided to use non-agricultural land because this does not reflect the productivity of the households at the moment. And also we use the number of migrants within household ethnic network. And the assumption is that the households better build their migration network on their ethnic affiliation. And an ethnic affiliation tends to reduce information costs. The knowledge we got from the keynote address this morning. And so if you reduce the information costs, then the households are using this to reduce the total cost of migrating and also meeting the requirement of the host countries. So one other thing that is very important in our work is the fact that is the fact of what we discussed yesterday with respect to Julie's presentation about remittances being noisy. And it's really very noisy because most of the households do not receive remittances even. But even those who receive remittances, there are wide dispersion in the distribution. So of course you know what this does in empirical estimation, especially in the family of OLS regressions. So we try... I don't know if we can answer. Okay, so we plotted the distribution of remittances. We received the amount of remittances received. But because of the presence of zero values, we could not plot them. So we added one to them and take a log. We take a log of one plus the amount of remittances received. And this is the first plot of the amount of remittances received. And you can see that the normal curve, which is the broken line, was doing a very bad job describing remittances. Partly because of this mass of zeros that you can see here. And also, and then we now discarded the zero values and then plotted the part B. And you can see this becomes a little bit more normally distributed than the former. And so we are model. So this is the basic model we run. And this is the log, the natural log of expenditure per capita of each household. And then we decided by variables, remittances, age and age squared of the household head, education of the household head. Importantly, the remittances variable is a dummy variable of whether a household have access to remittance or not. And again, the education variable is also a dummy which tells us whether the head of household has some education or not. So but we run this with the plantar regression and we used our instrument discussed previously to generate a random variation in remittances. So I better not discuss the data because that's already been discussed. Results. Our results was very conformative to the general result in this line. And we found that the receipt of remittances enhanced household expenditure at all levels of income distribution. In each level, the families that receive remittances spend more than families that do not. But this is not uniform. We found larger values at some point and lower values at the other point. And this is... But first, we apply the standard quanta regression to our model and what we found is basically what we assumed to be the reason why some studies had said that receivable remittances deteriorates income inequality. I mean, it generates more inequality in the society. But that standard quanta regression does not take account of the fact that households who already are any more are also more likely to receive remittances. So we applied the instrumental variable quanta analysis to account for the differential in access to remittances. And our results basically turned opposite of what we had in the standard quanta. And based on the instrumental variable quanta regression, this is what we have. We found that households at the 10th percentile of income distribution spend about 381% compared to other households who do not receive remittances. And this effect goes down as we move up the quanta but also goes up at some point. But most importantly for us is that this difference was largest in the 10th percentile. And then we found that... So this is... We plotted the impact of remittances based on the quanta regression by the quanta. At the 10th percentile, we found about 381% more than those who do not receive remittances. And then in the 25th percentile actually, we found about 207% more than those who do not receive remittances. And this is how it goes. The continuous line is what it would have been if we had applied OLS to the model. And of course you will see that this will not describe what is happening in the regression. So in conclusion, we found that remittances is actually more important for the poor households. But incidentally, these are the households who are most unlikely to migrate. And also, after we accounted for endogeneity, we found a strong equalizing impact of remittances on the distribution of income because the lower households in terms of income distribution are receiving more value from remittances than the rest. So, ordinary needs who are applied to this model would have performed poorly. And then even if we had applied quanta analysis without controlling for endogeneity, we would have continued to predict that remittances was an income distribution. But so in terms of policy recommendation, we have at least some policy recommendation, and basically it can be summarized by saying that we should open access to poor households, especially in developing countries. And there could be two ways to do this. One could be to improve access to finance, access to maybe loans that the households might use to overcome this cost, and then they could use this to supplement their wealth constraints and then be able to migrate as well. But secondly, one of the most important cause that they face is the restrictions from the host countries. In terms sometimes, in terms of education, sometimes in terms of other requirements that only they reach are more likely to access. So we would recommend that such restrictions could be a little bit lower. We know that this might be a very stiff thing to do politically because we can also consider how the poor households come into the developing countries. We compete with wages in the other countries. But there is some point that we should also consider. These facts will go a long way to reduce vulnerability in developing countries. And then this may actually reduce the need for foreign aid. And then when this happens, we can think that open migration might be a policy worth trying. Thank you.