 I'm very happy to present this work on the effects of COVID and inequality in Latin America, which is joint work with Ivana Severo, Maria Jose Cota and Miguel Sekely from the Centro de Estudios Educativos y Sociales of Mexico, and Francesca Castellani and myself from the Inter-American Development Bank. So what I'm going to show you today is first what we do with the 2020 household surveys. We look at those and we check what happened in COVID on inequality, and that's what we're going to do. But then I'm going to show you also a bit of the longer time trends and some current work that we're conducting on by looking at what happened in 2021, but not on inequality on labor markets, but as we know they're linked. So I'm going to also show you a preview of the current work that we're also doing. So general trends for Latin America and the Caribbean, well mostly Latin America and the Dominican Republic in this case, we see that this is a 30 years trend of inequality. We can see if you look at the lighter blue line, you see that inequality increased between 1992 and 2002 kind of steadily, so in the first decade there was a large increase in inequality. But then in the 2000 and specifically in these cases starts in 2002, we see a consistent drop of inequality, so we're moving towards more equality in the region. And this is related to many things. So first of all, you can see that the share of the poorest, the sale in the total income increases over time, and we see the same in the richest, I have a pointer, in the richest the sale that decreases, so you can see that this inequality is also related to the extreme part of the distribution. And also you can think that in the 2000s you had a lot of things going on for the region. You had some demographic changes happening, so a lower dependence ratio through time. You also had an expansion of the education system, so lower education premium that were going on, and also more favorable terms of trade for the region in general and the commodity boom, etc. But then what we're going to focus on is on the darker blue line, which has a very similar trend, but it's only for the 10 countries for which we managed to analyze the data for 2020 and look at inequality. And you see that during 2020 there was an increase in the Gini coefficient. It doesn't look, it's not as big as the Indian one. This is an average for the region, it goes from 0.49 to 0.50. But still, it's a 2% increase. And if you think of the increase that we saw in the first decade of this trend, where the average growth in inequality was half as much, then it becomes like a powerful increase. This is just to give you an idea of what's going behind those average trends. This is the story of, it's pretty much for every country. You see that the income share of the poorest increased pretty much for older countries that were looking at between 2000 and 2019. And the same story is very similar for the richest. For most countries you had the decrease of the share of the total income. And so as a result, you had that in most countries the Gini coefficients between these two years increased even though in Costa Rica and Colombia is not exactly the case. But for the rest of the countries, that's what we observe. And then during the pandemic instead we see that there is a general increase with larger heterogeneities among countries. And when we look at the percentage changes, you see that there are some countries, these are Peru, Bolivia, Colombia and Chile in which the increases in inequality were very large. And you have some countries in which the changes were not profound, Costa Rica and Argentina. Where I see Mexico and Paraguay, there is like a reversal. And you can think that perhaps 2019 was a specific year, you shouldn't compare those two. So we also show that when we take between 2015 and 2019 the average Gini coefficient and we compare it to 2020, the picture is the same. So we see that there is a general increase except for Paraguay and Mexico. And then we start to think about trying to dig deeper into these changes. And we estimate this equation in which on the left hand side we have the natural log of the income of the household. And then we have a series of dummies that control for gender, for the average age of the adults in the household. And these are dummies in which category they were. There was a young category, a prime age or an older one. The education levels and the urban and rural area. And we estimate this for 2019 and separately from 2020. And these betas that we observe for each country and year, we interpret them as the premium related to the gender, age, education, et cetera. And what we're going to calculate is how this change, this premium change between 2019 and 2020. And then to check sectoral changes instead, we take the hourly wages, except for Chile where we have only the weekly wages. But we take the wage of only the employed because we want to see also the sectoral changes if there were some sector premium they changed. And so in this case we don't look at the full household. We just look at the ones who are occupied, which means that the unemployed are excluded from these estimations. And what do we find? So first of all, the summary of what I'm going to show you is that there is a lot of heterogeneity across countries. So it's very difficult to summarize except for education, which we will see last. And so in some countries like Colombia, Chile, Bolivia, Ecuador and Peru and Paraguay, we see that the income premium related to households where the head of the household is a man, compared to the income of the households where the head of the household is a female, they increased. So basically these kind of households are very different as we know, but we see that the situation got even worse. And then if we look at the premium related to urban and rural areas here as well, we see very similar things to India. Basically in most of the countries we see that the urban premium that is generally associated to being in an urban area decreased. In most of the countries we see that it decreased because similarly to India and similar to most of the countries, the impact of COVID was more in urban areas. If we look at the age, we have very different, very heterogeneous results. So in some countries you see that the income premium related to the young population, to the average ages of 18, 29, compared to the 30, 44 age group, they diminished. And in some other countries the income premium related to the 45, 59 years old decrease. And by sector it's a bit puzzling what we find because we would have expected that the secondary and tertiary were affected more. And here you see that it's very heterogeneous. In some sectors you have that the secondary premium and tertiary premium mostly decreased, but in some others you have the opposite. And here probably depends on who remained in the sectors and the incomes that they had. Here what we find very interesting is that in general the income premium related to education, in the first graph you see having lower secondary education with respect to non-education, upper secondary with respect to non-education and tertiary education with respect to non-education and pretty consistently excluding Argentina, which is only Buenos Aires and Bolivia, you see that the education premium all decreased. And this is, we believe very much related to what happened in the labor markets of Latin America where you had a large share of employed people that left the labor markets. And so if you think that it's the most vulnerable who left the labor markets and so the lower productive and with lower wages and probably lower educated, then the ones who remain, thank you, the ones who remain are more productive. So probably this is something that is related to who remains in the labor market and is in the estimation. So if it is the most productive among the non-educated ones, that's what can explain this picture. And then we ask ourselves, okay, how did remittances and government transfers affect this change in inequality? Because we know from other reports that remittances, they stopped in the second quarter of 2020, the ones going to Latin America, but then they picked up again in the third quarter and the fourth quarter. And we know in general from the literature that remittances have a beneficial effect on reducing poverty, but the effects on inequality are more unclear. And when we look at what happens, so the light blue ones is the other genicoefficient, the change in the genicoefficient that we already saw throughout the presentation. The yellow is what would have happened if there were no remittances going to the region. And you see that the genie index are not very much changed, which suggests that probably the remittances were not tackling exactly the poorest, but were probably going more across the distribution. Instead, we also know that countries, they had huge fiscal packages to try to help the population, not as large as in advanced economies, but also in Latin America with a lot of heterogeneities, there were large fiscal packages. And it's very interesting to see that in some countries like Costa Rica, Ecuador or Peru, the fact that they had these transfers helped a lot to reduce the increase in inequality. They mitigated it a lot, because without those transfers, the inequality would have been much larger, which we found very interesting. And this is a preview of the other work that we are doing that anyway it's with more recent data. So I really wanted to show this is, we are looking at more countries. We have data for 10 countries again, but these are the ones in which we have quarterly data. So there are the masses pictures. So I wanted to show these ones. And so you see that the shock, this is the employment basically. So the shock in 2020, you see that there is a drop across all the countries. And then in 2021 it starts to recover. In blue you have what happened to women. And in yellow you have what happened to men. And it's very interesting, because we were wondering what's happening to women. And it's very interesting that in some countries you see that it starts picking up and sometimes even more for women than for men. It doesn't happen everywhere. But it's something that we found very interesting. And then you have some other countries in which instead the gap increased and it increased a bit in 2021, but still there is a bit of a gap. So again, very heterogeneous results. And this is some, again, some graphs summarizing a bit of the result. And when we look only at 2021, these are the gender, so we calculate the labor force participation and informality rates and another bunch of indicators for both female and men. Then we take the difference for each of these indicators. And here we are plotting how the gender gap in those indicators changed in 2021 compared to 2020. So here you see, for example, the Argentina in labor force participation. You see that there is, so for all these countries, not only Argentina, you see that there is an increase of labor force participation. So the gender gap changed favorably for women, which is basically the graph before. And it's not only in Argentina, we see it also in Guatemala and Uruguay and in Costa Rica. And here we see the reverse. And then the question is always, okay, they're more likely to be in the labor market, which doesn't mean there isn't a gap, it's just a change in the gap. They're more likely to be in the labor market, but do they get worse jobs or better jobs? Informal jobs can be a proxy of that, sorry. And here we see that he's not always the case. Sometimes even in terms of formality, so when you, I know here it says informality, but the ones that are in these quadrants, in the first and the two, are the ones where the gap changes favorably for women. So it's not always the case. And then we do something else that I'm not going to show you now, but we calculate the accumulated loss in incomes of 2020 and 2021 for women and for women. And anyway, we see that the overall loss in terms of income was larger for women. So even though there is a lot of heterogeneity and it's not necessarily that things got worse, still there was a big loss in 2020. And so concluding, inequality increased on average by 2% between 2019 and 2020. There is a lot of heterogeneities by country, education, gender, etc. Remittances had the modest effect in preventing greater disparities. And instead, government transfer played a big role. And as far as 2021 data is concerned, you have mixed pictures in terms of labor indicators, but still the income loss was greater for women. And that's it. Thank you very much.