 Thanks. So my question is for Nikita. I was thinking if you are also able to do some heterogeneity analysis. I mean for the relatively wealthier household or I don't know if you can look at in, you are looking at individual level, right? So you could check for buffer stock mechanisms and if the relatively wealthier can draw down their assets to just compensate, insure away the shock and they don't have to look for employment in other sectors right away. And for the females, one interesting thing would be to check their access to self-help groups if that kind of benefits them somehow. Since you have such a well-rounded data set, it would be great if you could contribute to that part of the literature as well. Thank you. Yeah, so I think the father is really to co-operate with the team. I think those are very, very good papers. I think a lot of effort, a lot of insight have gone into that. Yeah, so I think mine is just like a clarification from you. One is on the Jedi effect. I think I didn't get the title, I came a bit late. So yeah, so I thought that perhaps the non-farm activities that we are talking of could have also been affected by the drought. So that perhaps one would really want to see the issue of the income of men actually who are said to be really advantage because I think the way I could get is like men really are not really affected. Yeah, so I think how different were the incomes of men, I mean male compared to females because I would imagine that some of the activities especially in the area, in the rural area would be related to the farm. Yeah, so that is for the Jedi and the drought effect. Yeah, so in terms of Ajara, I think my problem is when you talk about measuring your effect of environmental issues like pollution and all that, health related and then you brought the issue of human capital there and you say that you are going to measure that or maybe a hypothesis that you are going to use hours, hours lost. So in terms of our lost, you also want to match that one with the wage for example. So my problem here comes in terms of the age structure for example. Yeah, so that for example we are talking about somebody who is not of age to participate in work for example, so there is no wage there. The same also applies to perhaps old people who are already out of job. I mean the kind of wage that you want to, for example, align with those people then is like zero. So then and of course given that we are talking about, is it Nigeria, the country which is led by a lot of informal sector and of course in some sector you have people who are actually not earning. So then my worry is like that of earning and the wage related the kind of mapping might dampen your analysis to some extent. I think that is in terms of hypothesis when you are explaining. So I think perhaps you might want to give us some insight on that. Okay then for the share, so I think mine is like the same concern you had that Jeannie is actually, Jeannie is actually improving growth. Yeah, so I think perhaps what do you think, what do you have to say about that is because that way then you are saying that Jeannie is good for growth, in college is good for growth and perhaps we don't go that direction. Then the other issue is in terms of government effectiveness. Of course I saw the government effectiveness there had a negative, a negative effect. Of course not significant but that does not always say that not being significant is not something that we cannot interpret according to the current literature. So what could be the effect because I think that is really going against a hypothesis that government effectiveness would really be very great for, would actually boost growth. I mean that could be the hypothesis. What could be the issue? Of course you might not want to interpret it because it's still not significant. Yeah, so that is what I have. Thank you so much. My question is for Nikita. I really enjoyed your analysis. I think two quick comments I had was I saw in your list of references, we had Burke and Embrick which kind of looks into the effect of climate change on US agricultural output. I was wondering if you are interested in kind of quantifying the short term effect as opposed to the long term effect and I saw that you're looking into deviation of precipitation socks over time. So have you thought about interacting precipitation with temperature and also kind of quantifying the relative short term effect as opposed to long term effect? I know maybe that's not, maybe that's a different paper but you know that's what I was thinking about. Thank you. Okay, so I had a question on whether wage effects in the macroeconomy would affect the inequality reduction and also the fact that some countries in the data set have a large portion of the informal sector. That's very true. So the idea of inequality convergence is measured through the aggregate income of the economy. So whatever wage effect exists, we can we can look at this estimation as a general equilibrium type of idea. So whatever wage effect that are there will be captured in the aggregate. So basically we are just looking at convergence in inequality at the macro level. So wage effect might not really affect that process because all that will be captured by the macro level data, hopefully that's the hope. But also in terms of inequality right and the environment. The mechanism is that when countries experience a lot of environmentally related impact on health, the quantity of human capital and the quality of human capital is reduced as a result of the environment. So if human capital like you and me, we have to be at school eight hours a day, 40 hours, 40 hours a week to work. But if you spend 10 hours of that 40 hours in the hospital because you had contaminated food or you drank contaminated water, that is a reduction in your income and it would go into the aggregate and reduce the aggregate income. Let's say about 50% of people have a reduction in their income, then the aggregate income or the GDP per capita for that year reduced and the Gini coefficient is computed based on that aggregate GDP per capita. So that is how the mechanism gets in there. So thank you so much for your very interesting questions. In fact, so Shushmita's point about heterogeneity, so that actually gives me time to shed some insight into the results we are getting in terms of heterogeneity, which I was not able to do because of the time limit here. So yes, we look at heterogeneity by the assets that they have, whether they have young children in the household, the social category that they're coming from, whether they're coming from the lower reserved categories of SCs and STs that we have in India. And what I'm really finding there is that most of this gender difference is coming for women that are more mobility constrained being women who are married, women who have young children, and women who are coming from the lower status of social categories. But the results are not driven by the economic status or by the poorer households compared to the richer households. So this gender differential is there, but in addition, your question was also about the overall impact. So yes, the richer groups are able to offset the impact better, but they're not able to fully offset it. So even they will have some male members of the household migrating and doing non-farm work. And if they previously were also engaged in non-farm, that's what we are finding. So richer group, they engage more in non-farm work compared to the poorer groups, but the dependence on non-farm goes up even more during drought shocks. So I hope that addresses your question. And regarding the excess to SHGs, which are self-help groups, right? So in our sample, every woman was part of the group. And it's a namesake part of the patient because it was, you know, like, since the government had launched this group thing, everyone was enrolled in it. But when I was doing my field surveys and interacting with those people on the ground, the SHGs are not really working because everyone is just enrolled. And there is no support coming through that. So that's why I did not look at heterogeneity by that because that is one for all women in my sample. And to the second question, and yes, that's very relevant, right? So the income in the non-farm sector, right? So in terms of the descriptives, if I look at the absolute income in-farm and non-farm work, they are higher for men in-farm compared to women in-farm and in non-farm compared to women in non-farm. And if I just compare across form and non-farm, again, the income in non-farm are higher compared to that or form for both the gender. So when men are taking up these non-farm activities, they are earning more than they were previously earning in-farm activity and which is also higher than what women will be earning. And even if drought has an impact, so the direct impact is on the form sector, there are indirect impacts possible on the non-farm sector also. And as Emerick, like in Emerick's paper, he does talk about this local demand having, you know, the spillover effect on the non-farm sector. But when we look at the relative changes in these earnings, the fall is larger in terms of the farm earnings relative to the non-farm earnings. So that's why I said that the non-farm sector is more resilient or I would say less affected by the drought shocks compared to the farm sector. And to the last end of, you know, it's very important, yes, persistence, right? We're talking about whether shocks work about climate really and in the long-term what is happening. And that's why it's, that's what the extension, the ongoing work is to understand this year you were hit by a weather shock, you lost job, women were out of the labor market. Next year there is no such shock. Are women coming back? What is happening to their long-term participation? Does one time exit because of these weather shock? Does it have a permanent effect or is it a temporary impact? So that's really, you know, the idea. And in our specifications, in a robustness check, we do look at the interaction of precipitation and temperature. So one way is to have the negative productivity shock being captured by temperature shock itself. And the second was to have controls for temperature in addition to precipitation. And, you know, we get very similar results here. I would like to add that you can add drought beside that humidity, relative humidity, because its temperature humidity is much persistent for then the precipitation sometimes. Yes, absolutely. They call it the temperature. So yes, no, absolutely we can do that because the thing is, you know, you can throw in a number of things but at the same time they are very correlated at the end of the day, right? So drought and temperatures, they are themselves highly correlated. So yes, I can definitely try the humidity measure also because that is also available with that data set. So, yeah. So thank you. Very interesting questions. Okay. Thank you so much. For the last one, I really appreciate your comments and it's a working progress. So we're seeing what's going on with the data set and dealing with three equations. It's very hard to deal despite the fact that the literature can have a different explanation. Sometimes inequality can boost growth or it doesn't boost it or there's a negative relationship. So this is available in the literature. So the literature for the relationship between growth and inequality is a little bit ambiguous. So there is no clear sign that we can say yes, it goes that way. So, and as well as we are going to look for different content, the results for different content, the different income groups. So we're still experimenting the simultaneous equation models. So thank you so much for attending. So I think you had a positive relationship between human development indicator and heat. I think that positive relationship is not a causal relationship because what you see is that globally human development is increasing. The quality of human capital is increasing. So the fact that it's increasing is exogenous on its own. And the fact that climate or weather related problems are happening, those are also exogenous on its own. They are completely exogenous factors. So I don't see any theoretical backing why we should regress human development indicator on a climate related or disaster anything. There is no theory that can link the two. So I think these are two exogenous effects. That is why there is a positive relationship. It's because they are talking to each other in a different way. They are all moving upwards globally. So that is what you are seeing. So the result is correct, but I don't think the human development indicator should have been in the model. That's just my point. And I had the same point. That's what I did. It's probably we are thinking on the same lines. And you know, in addition to that, I wanted to say that in terms of the number of hits they are experiencing, it's possible that they are adjusting in terms of the investment. That was some insight coming from your paper also. That's how it ties well with that finding because it's depending on the kind of exposure that you are getting. Also the type of hits you are experiencing, a coping mechanism would be to invest in these human capital for missions. So probably that is what you are capturing. And there is a lot. It's not causal for sure, but you might want to dig a bit deeper into the reverse relationship of hit having an impact on the HCI. And another question to you is about, so all of your analysis, citrus perivus, it was about if we assume that other things are the same, how it will have an impact, in how many years we will see those convergence divergences and all right. But my question really was of all the previous years of data that we have seen, how much has actually been following the same rate because given that depending on whatever level you are on, the policies are always evolving on the basis of that level. So if that is the case, then it is possible to construct bounds around the estimated number of years using that information. And that will give us a better picture. Okay. So it's not 90 years, but it ranges from 80 to 100 years. So do you not think that would be a better approach to? Yeah, I think it would be fine to create a boundary that find this is not exact, but it will fall within this bracket. And that would mean that I need to choose a different rate for each of, so I need to have an upper rate of accumulation and a lower rate of accumulation so I can develop that bound. But what I currently have is the average of the two, the average of the lower bound and the upper bound. That is the result that is there. So I think that is a brilliant comment so that I can have those two to give the viewer's perspective as to where the data falls. So yeah, I think I can incorporate that. Thank you. Okay. So any other questions? Any other comments? Okay. Thank you so much for being here and listening to the interesting talks. And have a good day. Thank you.