 Thank you very much. I think I'm going to be very brief. I'm going to present this paper with my co-author, David Castells Quintana from the Universidad de Barcelona, and I'm José María La Roo from Universidad de San Pablo in Madrid in Spain. I have developed this paper for the last two years and of course it's a draft. Everything is a draft. My motivation and framework is you can put the paper in the framework of growth inequality and poverty triangle. So I think there is a lot of research in the growth and aid relationship, especially due to FinTARP, and well it is a very controversial result still, but I think that most of us could accept that in general terms or in average there's a positive relationship between aid and growth. Of course it will be a very negative relationship in the absence of growth and poverty. It's very controversial still the relation between growth and inequality, but my point is the direct line. There is very, very scarce analysis and literature on the relationship between aid and inequality. Maybe the main channel should be that, A, if it is very well managed and focused on poverty could reduce inequality, maybe because income inequality of course and within country income inequality, because they focus on poor, they can start up the incomes of the poorest in the country, but we can detect a lot of indirect effects that could affect the aid and inequality relationship. So the outline of the paper, we have some literature review among inequality in Latin America. Yesterday we have a presentation with Cornelius, the expert of the issue, and aid as a possible determinant of inequality. We have to explore very, very little literature review and theoretical channels. I will be a little bit more deeper in this point. And after we have developed empirical evidence and econometric exercise, we have some data and trends on genie and aid, and our methodology was three steps. We start with trying to replicate Chong and other co-authors that maybe they have the first approach for the aid and inequality relationship, but we extend the controls and apply our extended model to Latin America because it's the most inequality region in the world. So we have some cross-section and pool cross-section, and afterwards we have some exercises with panel data. Briefly, inequality in Latin America as it is well known is the most unequal region in the world maybe, but it's discussed. We have some words trying to show that historically in Latin America has not been a very, very high inequality. But nowadays we have our period of studies, 1992, 2008. Most of, but not all, of lower income countries in Latin America are very unequal. So we have in the yellow bars the group of lower income countries. And the evolution that is very well documented yesterday by Kornia has, well, very, very terogeneous. We often try to think about Latin America's homogeneous region, but the difference among countries are very, very high. So Colombia has not present any reduced inequality, but Ecuador, Peru, Argentina, El Salvador, a lot of countries have experimented a very high reducing inequality. The causes are macro policies that trade openness. So just some contra-revolution in liberalization policies in, or since 2000. The fiscal policy, this is so called a new fiscal part with some progressive taxation and very focused conditional cash transfers. And the lower skilled labor premium in some countries has increased the average years of schooling, employment for lower skilled workers, especially are demanded for the so-called maquiladoras, and higher minimum wages in some countries, for instance in Uruguay. And standard flows for indicted investments and remittances. But note has studied ODA. In our sample we have, for most of the countries, annual observations of Gini, thanks to the compilation of Martorano and Cornea in 2011, and a very persistent trend in the Gini coefficients. And aid in Latin America is, well, it's high for some countries, especially for Central America, Paraguay, and Bolivia, but very, very low in relative terms of GDP for the bigger four, or Mexico, Argentina, Brazil, Chile. So it's a very terogeneous map. And in relative terms, for instance in public expenditure for Nicaragua, it's very, very high. We have data for our sample. You can see the average for the period in Bolivia, 8.7 percent of GDP. Nicaragua, the highest, 24.6 percent, but at 0.01 in the big four. So our literature review, there's various cards, articles. Some authors have found a positive effect of aid on inequality. Another one, no effect, especially our main article that we follow, the methodology or the inspiration was Chong and Quo first, but they have studied for all countries in the world, not the focus on Latin America. And the period was in 1972 to 2001. So we are more related to the present, to the nowadays. And some have detected negative effects of aid on inequality. So this is, this is a puzzle or a scheme. We have, we try to think about how aid could affect income distribution. Of course, aid is, we can channel aid through bilateral, multilateral and multilateral or international organizations. We say it could be received by central state or north central state or local NGOs. Instruments are very, very different, divided by the support, relief, technical assistance, projects and programs related to macro stability. For instance, structural programs for multilateral banks. And we have tried to analyze and disentangle different sectors, social sectors, but even economic infrastructure, production, multi-sector, and even it could be received in in-kind transfer for a, for instance, or cash transfer credit or knowledge and know how such a technical assistance. And the effect could also depends on the role of the elite of the middle class, how widely the middle class is in each country or the poorest. If aid has effectively a focus on the poorest region, the poorest group, the poorest zones of the country. So some, some selected channels. For instance, we can think about better governance about aid that has focus on capacity building and trade unions that could lead for minimum wages or collective negotiations. We can think about macro stability of the multilateral programs that could lead to lower inflation, better real exchange rate of terms of trade that improve the investment climate and attract foreign-rided investment or dead relief that of course free resources. Nicaraguan, Honduras are hippie countries divided by the support that could feed CCTs programs, conditional cash transfers programs, and technical cooperation that could expand or enhance fiscal reforms. So our simple correlation of our first step, we can detect a positive relationship between the Gini coefficient and ODA in related terms on gross national income. Of course, Nicaragua is an ally, but if we take out the Nicaraguan case, the positive correlation on average is still maintained. But country by country, we have eight cases for negative correlation, especially Uruguay with minus 5.1 and square error of 0.45. Brazil is not significant. And nine kinds of positive relationships. So, as it is well known, correlation is not causation. So we have two control for other variables. So we construct a simple model and we introduce foreign aid and control for other channels or variables that could affect inequality. We select the four comparable results, the chunk and control, inflation, liquid liabilities, literature, GDP per capita, agriculture, and industrial in percentage of value added, but we added some domestic redistributed policies, public expenditure and social expenditure, labor institutions and education as a second vector with minimum wages for the formal employment and employment and the Gini of education. A third vector is a standard redistributed flows, terms of stress, FDA remittances, and foreign aid in our case, and political contents through the political indicator, the quality of the democracy. So we have the correlation matrix. We don't have very, very high correlation among the variables. And our first result is a cross-section. We replicate the estimation or the identification strategy of in the first column of Chonan quotas. And in this case, aid is not significant, but when we introduce with the same controls but adding public expenditure, aid is now significant in statistical terms and negative. And this coefficient maintains the sign and the significant level when we are adding more controls, the minimum wages, the quality to the social expenditure, terms of trade, FDI or remittances. Well, this is a bad you can see we only have 18 of them, I assume, because we only have 18 countries. This is the first step. Our second step. So we will pull the cross-section and replicate the same model of a chunk where aid is not significant when we use the same controls, but as with the simple cross country, when we add public expenditure, aid is now significant and negative. And it's maintained as the sign when we span our controls. Especially interesting could be the significant sign of FDI, positive and remittances negative with an equalizing effect. Now we have 108, not too much observations. But when we use our best econometric specification or technique that is system GMM, because we are dealing with atrocity, we are dealing with the persistence of the genie, we have very low observations by country, but maybe enough number of observations in the whole sample, 184. In the first two columns, the results are related to a triennial average of the values of the variables and the last three, three, four and five columns, we have annual observations. As you can see, the sign and the significance of the foreign aid is still maintained and negative. So we have some robustness checks. In the cross-section, we use the last genie observation, the 208, instead of the average genie for the whole period. We try to, instead of use the genies of cornea and corneas sample, yes, to use the ZLAC, the ZLAC for genie, entropiae and so on, but we have a lower observation to 130 observations and we have 323 genie indices in our sample. And instead of use aid as a percentage of GDP, we use per capita ODA and the results still maintain and gross disbursements of in dollar, in cost and dollar terms with and without debt relief. We try to use country programmable aid, but this is more focused on the money that the recipient countries are received, but the problem is the data is only for 2002, 2010, so we don't have enough observations to use the GMM system. And the conclusion, maybe we have to consider aid as an egalitarian external flow, no only remittances or foreign aid investment. I think it's very, very important for Latin American region, because for example, European Union are taking out Latin America region for their ODA flows, stepped IT, and maybe we are not considering the Fed-off on inequality. But we have to go very cautious with the multiple channels, maybe the impact should be indirectly, because there is complex transmissions, maybe we have to go my last point country by country, now we are studying the four Central American countries and Bolivia, trying to use the, first of all, the aid that the country has computed as received. No, the OCDA figures, no the donors that know what are saying the donors, but what the recipient countries has a compound or computer as effectively received, and trying to study if different donors have focusing on different departments within the country, especially in Honduras, where we have better data, but we are now collecting data for Nicaraguan and Bolivia as well. Thank you.