 Well, this is a paper that is going to appear on a book of the effect of COVID-19 on income distribution. The paper, I'm the leading of the paper, the corresponding author, but there are many colleagues contributing to the paper from seven countries in Latin America. The idea here is to analyze how well the tax and benefits system work during the pandemic. The idea is trying to decompose the effects of changes in market incomes and the contribution of taxes, the contribution of benefits, and also the contribution of new policies that were implemented during the pandemic. The idea is we have seven Latin American countries, Ecuador, Colombia, Mexico, Bolivia, Argentina, Peru, and Uruguay. This is a great variety of countries. We have, for instance, Uruguay, that is the most redistributive country in the region, but also we have countries that redistribute less, for instance, Bolivia. So this is, generally speaking, a variety of countries for the region. And the other thing is that there is also a high degree of difference between the effect of the pandemic on labor market in the countries of the region. So there is a great variety to analyze here. We know the pandemic brought a lot of problems, an increase in unemployment, a huge deterioration of the labor market, and an increase, obviously, in poverty and extreme poverty. So we want to check what happened with the tax benefit system and how the new policies cushion this pandemic and how these policies affected the distribution of incomes in the region. This graph presents the changes in employment levels. We have separated the analysis for two periods, the second quarter of 2020 and the last quarter of 2020, and we have, as a baseline, 2019. We can see that regardless if you take into a conformal or informal employment, there is a huge drop in employment in the region. For the last quarter, there is an important recovery, especially for Argentina, but in other cases, employment levels are below pre-pandemic levels. The idea is we have tax-benefit micro-simulation models for these seven countries. Some of them belong to the South Mod family. For instance, Bolivia, Colombia and Ecuador belong to the South Mod family of models. We also have models for Argentina, Uruguay, Mexico, and Peru, Colombia, and Bolivia and Ecuador for South Mod. The idea is all these models are implemented using Euromod. What we do is we have these models built for 2019. We also compute the policies in Euromod for the two quarters, Q2 and Q4. We have mostly data for 2019, some data for 2020, Q2. There is an exception here. We have for Mexico information for 2018, so we move this data to Mimic for Mexico, the data in 2019. For the other countries, the thing is that in the second quarter, the statistical agencies in several countries didn't make the whole survey. We have only information on their earnings distribution. We have only micro-data on earnings. What we do is now cast incomes with 2019 data. We use this earnings distribution in 2020, Q2, and with a statistical method, we use a probid and an average among groups to update the data of 2019 to look like 2020, Q2. So this is the now cast, and the idea is we are covering the policies that were before the pandemic 2019, but also we are modeling the full range of new policies introduced in the countries. Mostly during the pandemic, there were introduced benefits, but also, for instance, in Colombia, we also have changes in social insurance contributions and also solidarity tax for high earners. It was implemented in Q2 in Colombia. So there is a mix of new policies implemented during the pandemic in the region. So getting back to the now casting, the idea here is that we try to mimic the distribution of incomes in 2020, Q2. We only have information on earnings, so we take 2019 data. We chalk the earnings distribution of 2019 to make it look like the distribution in Q2 in 2020 to Q2. For that, we use firstly a probid. This probid is going to tell us which observations in 2019 are going to lose their jobs or not during the pandemic, as we are trying to mimic 2020 Q2. But after the probid, if someone is predicted to keep their earnings in 2020 Q2, the idea is that we adjust earnings to mimic the distribution of earnings in 2020 Q2. How do we make this adjustment? The idea is that we make groups by industry employment and formality, and within these groups, we calculate the average income, the average earnings in 2020 Q2 and also in 2019. And we compute the difference, the percentual difference between these two periods and update the 2019 data to 2020 Q2. The other thing that we do is we try to decompose the effects of different things. There is the effect of the COVID pandemic itself. So we see a drop in employment, a drop in earnings, so there is this market income effect. We also have the policies, the pre-COVID policies in each country. So for instance, if people lose employment, they are going to stop paying social insurance contributions. So this is an automatic stabilizer effect. The other thing are the new policies implemented during the pandemic. So the idea is to make this the composition. We have the baseline that we compute for which we used the 2019 policies. We also have the 2020 policies. We can apply these policies to the 2020 Q2 data and with the policies, without the new policies to see the effect of automatic stabilizers. And so in the graphs that I'm going to present, I'm going to decompose these effects in between earnings, automatic stabilizers and emergency policies. The other thing is that I'm going to do this also for 2020 Q4. In this case, we have actual information. So at the end, if I have time, I'm going to present some validation results for the now-casting approach that we are using. So this is, I think, the most important result for our paper. This is a weighted average of the effects of these three components for Q2. So how we read this is as follows. We have, towards the negative values, we have earnings. So we see the drop in earnings by each detail. These are details of household disposable income per pandemic. And the idea here is that for the region, we observe 30% drop in earnings. So this is like the market effect of the pandemic. The dark blue bars indicate the effect of COVID policies. So mostly in the region, we have new benefits, especially target to the poor, because in the region, the idea is that most cash transfers are targeted to the poor. So we see that on average in the region, 25% of pre-pandemic disposable income represents the effect of policies in the first day of sale. But we see that these transfers are especially at the bottom of the distribution because they are focused mainly on the top of the distribution. We don't see very important effects of COVID policies. The other thing is automatic stabilizers, as we have this counterfactual scenario in which we have the pre-pandemic policies, but the pandemic data, we can see how the previous, the previous, the pre-pandemic tax and benefit system cushioned the effect of the pandemic. And we see that the effect of this automatic stabilizer is increasing as we move to the upper part of the income distribution. So for instance, the automatic stabilizers are almost null at the first deciles, but are about 5% at the top of the income distribution. This is because, as is typical in developing economies, formal work is at the top of the distribution. Formal work is paying social insurance contributions. So when the shock comes, several workers especially at the upper part of the distribution are going to have lower earnings, and they are going to contribute less to social insurance contributions. This is the automatic stabilizer effect. And with the circles, we have the overall effect, so the effect on disposable income relative to disposable income in 2019. So for instance, for the first decile, taking into account the COVID policies, but also the drop in earnings, we see that the effect is new. So this tells us that the COVID policies were effective cushioning the drop in earnings at the bottom of the distribution. But we see that at the upper part of the distribution, the net effect is negative. So for instance, for the second decile, the drop in disposable income is about 20%, and it's more or less the same up around the distribution. So the main message from this graph is that the COVID policies were very focused on the very, very poor, extreme poor, we could tell the extreme poor, but in the upper part of the distribution, the policies were not that effective. This is the same graph, but we have the analysis for each country. And the idea here is that there is a huge degree of variation between these three effects. We have Argentina, Bolivia that implemented a lot of COVID policies, but we also have Mexico that implemented no COVID policy. We also have a high degree of variation in earnings, for instance Peru has a huge drop in earnings, but Argentina and Uruguay doesn't have this huge drop in earnings. And also, for instance, in Mexico, automatic stabilizers play a bigger role than in other countries, especially because there are drops in income tax payments. From the poverty and inequality perspective, what we can see here is that in the second quarter inequality increased a lot for most countries, and the COVID policies had a reduced effect, decreasing this increase in inequality, also in poverty. For instance, I don't know, Colombia had a baseline, a genie of 0.5, it increased it 5 percentage points, but taking into account the new policies, the idea is that this inequality drops only 1 percentage points. So the effect of the tax benefit system and the new policies were very reduced in the region in terms of inequality and also in terms of poverty. We also have the same results for Q2, the idea is the drop in earnings in Q2 is lower because for Q4, the idea is in Q4 the economic activity recovers, employment increases, so the drop in earnings relative to 2019 is smaller, it's about 5%. Also, there is a reduction in the emergency policies that were implemented. For instance, in several countries for Q4, we don't find any COVID policies, any new transfers to households, so the effect of the new policies vanishes as well. This is the results for all countries and also we see that there is a high degree of variability for Q4 for each individual country. The case of Colombia and Uruguay are interesting because some policies remain even at the end of the 2020 year. Okay, the inequality and poverty, the idea is the same, inequality and poverty increases but the effect of the tax and benefit system is very reduced. So I think this is the main message. The idea here is that the tax benefit systems in America and Latin America are not designed to face this kind of extreme event that was the pandemic. So the region needed these emergency policies but the emergency policies were focused at the very bottom of the distribution and not for instance in, I don't know, the third, fourth and fifth this aisle, so the effects of the policies were not that important there. So for future work we are trying to analyze how to improve automatic stabilizers in the region because in the face of such a crisis it seems that they didn't work in Latin America. And I think that's all. Thank you.