 Good morning, everyone. Thank you for being here. My apologies for my sore throat. I was not prepared for the weather, so, but hopefully you'll follow. The work that I'm going to present today is, has been done jointly with Rahul Lahoti, who is at UNEO Wider, and it's titled who was impacted and how COVID-19 pandemic and the long uneven recovery in India. So before I start with the paper itself, let me give you a sense of how the pandemic manifested itself in the country. So this graph, this picture is telling us three things. The first thing can be read from your left axis, which is the COVID-19 cases reported between 2020 and 2021. The right axis gives us the percentage change in visitors. These are basically Google mobility numbers, and we report them at two places, one being retail and recreation, places of retail and recreation, and the second being places of workplaces. And the third thing are these shaded regions that you see. So the first, the shaded region between March, I'm sorry, February, March, no, March, April and May, I'm sorry, 2020 is the lockdown phase when India introduced a nationwide lockdown. It was implemented in the last week of March and went on till mid-May. Thereafter, the second shaded region is the post-lockdown phase, which goes till March, 2021. So it starts in June, 2020 and goes up till March, 2021. Thereafter, you have two months of the second wave, which is April and May, 2021. And thereafter, up till December, 2021, we have what we call the post-second wave. If you look at the COVID cases, which is this red, deep red line, we see the, let's, I mean, I'll just be short. The main takeaway is that, well, the cases peaked for the first time around September, 2020, which was way after the implementation of the nationwide lockdown. The second wave, of course, was far more devastating and the number of COVID cases and deaths went through the roof. If you look at the Google mobility numbers, we see that, of course, everything dropped and there was about close to an 80% drop in footfalls in both places of retail and recreation and workplaces. Thereafter, as the economy began to open up, we saw that mobility increased. It, again, dropped during the second wave, though the magnitude remained much lower than what was seen during the first wave. And thereafter, now, by December, 2021, we are really back to normal time periods. So this is how the economy was looking like in terms of mobility and COVID numbers in India. Now, if you want to talk about the economic impact of the pandemic, it makes one way to do so is to classify it broadly into categories and we look at the employment impact and the income impact. Our paper here tries to document the immediate impact of the pandemic on income levels, income inequality and poverty. And we go on to track the nature of recovery all the way till December, 2021. Given that things may get rushed towards the end of the presentation, I would like to give you a preview of the results. We find that incomes per capita household incomes were much worse affected in the urban areas as compared to their rural counterparts. We find that the poorest segments of the population in the months of April and May, 2020 were the worst affected. They lost their entire income in these two months. Recovery was sharp, but it has continued to remain incomplete and uneven even till December, 2021. The poorest deciles in the population recovered rapidly. While the richer deciles, they saw very little recovery, if at all. So this stagnated. There was a very sharp rise in both poverty and inequality during the lockdown and it continued after. However, till the end of our period, we find that inequality has returned to the pre-pandemic levels, although poverty continues to remain elevated. Okay. The data set we are using is a household survey data set called the Consumer Pyramids Household Survey data set. This is collected by a private agency called the Center for Monitoring into an Economy. The data set is claimed to be nationally representative and it's high frequency panel survey data wherein every household is interviewed three times a year. In doing so, we get income, we get employment information for every individual at three points in a year. We get income information for everyone for every month. And that's what we are going to use. The household, the survey is, so the agency manages to survey around 200,000 households which is equivalent to around 900,000 individuals across the country. We are interested in household income and we'll be working with per capita household income which is our variable of interest. We'll be talking about three income metrics. I'll go one at a time. The first thing we do is a simple trend analysis wherein we are tracking the monthly income, average income over time. We do this between January 2018 and December 2021. So the access here on the Y axis, we have the average monthly income in constant prices and we plot that over time for all India as well as for rural and urban sector separately. We find that of course there was a very sharp drop in incomes in the month of April 2020. Please note April 2020 is the first month, first complete month of nationwide lockdown. Thereafter as the economy, also the drop in incomes was much sharper for the urban areas which is given in the red line as against the one in the blue line which is representative of the rural sectors. As the economy began to open up towards the end of May 2020, of course incomes began to climb back up. But if you look at this point, these peaks which is January 2021, we find that even by January 2021, income levels had not gone back to the pre-pandemic level. What I mean by the pre-pandemic level is essentially the levels in February 2020 which was the last normal month if you will before the lockdown. Now the drop in incomes was again witnessed for the second time. It was much lower, much smaller in magnitude. But this drop in income came at the back of the first fall in income. So it wasn't that incomes dropped, recovered and then dropped again. It was really a double whammy and even by December 2021, incomes are almost back to February 2020, but not fully. So that was the trend analysis. Now, given the nature of the crisis, there was a lot of fluctuation on a monthly basis. We are capturing the monthly level here. And to kind of smooth that out, we also do the analysis at a cumulative level. For that, we split our entire time period into five, which I have defined here. So the pre-pandemic period is defined as the period between January 2019 and Feb 2020. The lockdown period is March, April and May 2020. Post lockdown is June 2020 up till March 2021. The second wave is April to June 2021. Post second wave is July to December 2021. These are growth incidence curves. We do them separately for the rural and urban sectors. I will take you through the urban sectors. The takeaways are broadly the same. So what we are plotting here is the proportionate change on average per capita household income for every percentile, for the different periods, the periods which I mentioned right now, the five periods. So your y-axis here gives you the proportionate change in per capita income. If you look at this red curve, that is plotting the change in income in the lockdown period as compared to the pre-COVID period. And if you notice, the line looks severely regressive in nature in the sense that the poorer you were, the sharper was your drop in incomes, or the change in incomes was much sharper as against your pre-lockdown income or pre-COVID income. Note that this entire thing is in the negative quadrant. So the bottom five percentiles of households were really making, I mean, their incomes dropped 100% as compared to the pre-COVID incomes, while it was much lesser for the top percentiles, the 100th percentile. This is for the, this is when we compare the lockdown period with the pre-COVID period. Now let's go forward and let's compare the post-lockdown period with the pre-COVID period. There we see that the regressive nature of the impact really kind of goes away. This is, it's given by the blue line that you see here. Nevertheless, everyone was making lower income in the post-lockdown period as compared to the pre-COVID period. Then comes the second wave. And in the second wave, again, we know that incomes dropped. However, so there was a slight regressive nature to this yellow line. I'm sorry for the color, it's not very visible. It was not as bad as it was in the lockdown period, which was the first wave. And finally, thank you. And finally, if you look at the post-second wave and pre-COVID period, if you compare those two, we find that incomes continue to remain around 15 to 20% lower than what they were in the pre-COVID period. These are our poverty numbers. So we do this analysis by different thresholds. I will be talking about the national minimum wage here. If I take the national minimum wage to be the poverty line, in the pre-COVID period around 32% of the rural population and around 18% of the urban population were lying below the poverty line. Of course, things were, they shot up the roof during the lockdown period. It more than doubled or doubled in the rural areas. What's noteworthy is that even in the post-second wave, which is really after about two years since the lockdown, the numbers continue to remain inflated. So it was around 32% for the rural sectors. It is still at 36%. It was 18% for the urban sectors. It is at about 24%. These are the populations lying below the poverty line. The story was a little different for income inequality because we find that while it really shot up in the lockdown period, it was 0.44. The Gini was 0.44 in pre-COVID period. It shot up to 0.51 during the lockdown period, but by the post-lockdown, post-second wave period, we find that the income levels have gone back to the pre-COVID levels. This is in contrast to the poverty story. Okay, the third thing we do is that we use an event study framework to determine the impact of the pandemic on household incomes. While we control for different characteristics, we do not claim them to be causal because there was no counterfactual really to compare it with. But to get a better understanding of the economy, we do the analysis for different heterogeneous groups and see how things look like. That's the equation. I'll quickly take you through it because I'm running short of time. The YITJ variable that you see here is the income, the seasonally adjusted income for household I in month T for group J. By group J, I mean these heterogeneous groups. So for all India, J is all India, for region J is rural urban and so forth. And the Y bar, delta J, that is essentially the average income in the month of February. So the Y, the LHS is giving me the proportionate change in income in every month as against the base month, which is February. We are really regressing this on time, but this is just an index time. I can talk about it later and we have household fixed effects and error terms which is clustered at the household level. Okay, so quickly, I want to focus on the later part, but we see that in the urban, so Y axis is the proportionate change in income vis-a-vis February. The dotted line is at February. The dotted vertical line is at February, so everything is vis-a-vis that. We see that in the urban sectors, the drop was far more sharper as compared to the rural sector. We do this also by deciles. It's done for both rural and urban. I'll talk about urban. Some quick takeaways. Again, the Y axis remains the same, but what we see is that the drop in incomes that we are seeing in again the month of April, 2020 is beautifully monotonous in nature, which means that the poorer you were, the sharper was your drop in incomes vis-a-vis your own February incomes. However, the recovery too was of a... It kind of flipped. The sharper was your drop, the faster was your recovery really. And if you focus on the top deciles, we see that they lost about 20% of their income in April as compared to the February incomes. However, they kind of have stagnated. They don't really have seen much of a recovery. And we hypothesize that the reason for this is coming from the nature of occupations that they have been involved in. So if you belonged to contact... If you were doing jobs which were more contact-intensive, they were more informal in nature, they had less security. We'll take the example of daily wage work. We know they were impacted severely during the lockdown because there was an overnight loss of jobs. However, we also know they recovered quickly as soon as the economy began to open up. Now notice, these are households. So if you think of daily wage workers, these are households which also typically populate the lower designs. They belong to the lower segments of the population. And the lower designs were the ones which suffered a sharp drop in a sharp recovery. Similarly, if you think of more formal sector jobs, I'll take a minute. If you think of formal sector jobs, there we see that these are typically households which belong to the right end of the distribution or the richer segments. These deciles also saw a muted drop, but a slower recovery. And that's what we see for white-collar workers. I'll skip over this heterogeneous groups. There are several issues with the data set, which if anyone is interested, I can talk about it. But I'll skip right now. So summing up is that you know the preview, so I'll stop. Thank you. I'm sorry for taking up more time.