 Welcome to the first keynote lecture in the wider development conference 2021. I'm Kunal Sain, the director of Year New Wider. I'm delighted to introduce the first keynote speaker. Oriana Pandia is a Sir Anthony Atkinson Professor of Economics at London School of Economics and a fellow at the British Academy, Economic Society, CEPR, BRAD and ESA. She's co-editor of Economic Tricker, Vice President of the European Economic Association and Director of the Gender, Growth and Labor Markets Low-Income Countries Program. Oriana serves as the Council of the Economic Society on the Board of the International Growth Center and as the Vice President of the Collegio Carlo Alberto. Oriana's research focuses on how monetary incentives and social relationships interact to shape individual choices within organizations, how this shapes labor markets, the allocation of talent and ultimately living standards. Her research has been awarded the IZA Young Labor Economist Prize, the Carlo Alberto Model, Nettle, Esther Bosto Prize, the Yaro Johansson Award and the IRO Award. One of the profoundly negative implications about the pandemic is that it will set back progress in reducing poverty by 20 years or more. Oriana will draw from a recent research to speak of the implications of COVID-19 for the extremely poor in low-income countries. She'll speak for about 45 minutes and we'll have 15 minutes at the end for Q&A. Please send in your questions in the Q&A feature and I will read the questions on your behalf. I now invite Oriana Bandiera to provide the first keynote lecture of the conference. Oriana, over to you, thank you. Good morning, my name is Oriana Bandiera. I'm from the London School of Economics and I'm very grateful to the organizers for inviting me to give this keynote today, talking about COVID-19 and extreme poverty. Now, it's probably no news to you that for the first time in 20 years, the World Bank has predicted a large increase in global poverty. And this figure makes the point very clearly, global poverty has been falling steadily for the last 20 years. Here is a very recent data from 2015 projected up to 2021 and we see that the fall in global poverty has been halted because of the pandemic in the year of the pandemic and the projections are such that basically five years of gain in poverty reduction are gonna be completely unknown by the pandemic that we are experiencing in these days. Now, these are global numbers. If we look at the poorer regions of the world, in particular, sub-Saharan Africa, where poverty was not falling before the pandemic, we actually estimate a net increase in the number of global poor living in sub-Saharan Africa. And the numbers are quite large. You see that you go from an estimated 440 million of extreme poor to almost 500 million. So where things were improving, the improvement has been stopped, where things were not improving, the estimates suggest that poverty will get worse. Now, to some extent, this is not surprising because we're living in extraordinary economic times. Economic activity has been heavily disrupted by the pandemic. So it's rather obvious that when things go bad, all the bad things get worse and poverty is clearly one of them. But the really question of interest is whether this effect is permanent or whether it will revert once the pandemic ends. So the answer to that question depends on the structure of the economic activity in countries where most of the world poor live. And in particular, whether there is a mirror image of the big push phenomena, which I will call big pull. So to remind you what we know about the big push is that policies that represent a sufficiently large positive shock for the poor can actually break the poverty trap and lead to a permanent reduction in poverty, which comes not from the fact that you give a large transfer, hence people have more to eat, but rather that large transfers enable the poor to take actions which will take them out of poverty forever. And these actions can be investments in human capital that allow the engagement in more remunerative occupations or investment in physical capital that likewise allow the poor to shift from bad, poorly paid jobs to better paid jobs. So fundamentally there are two ways in which people can get out of poverty because poverty is thought logically not having high enough earnings. How to improve earnings you can invest into physical capital with which to combine your labor. So for instance, a casual agricultural laborer can invest in land and combine his labor with land to produce more and hence earn more you can invest in human capital so that you can leave the farm and go work for say a farm that will pay more. So to reduce poverty rather trivially we need people to have higher earnings, to have higher earnings you need to combine your labor with other physical or human capital. Now the question is whether the pandemic will do the opposite of these big push policies that is will take away physical capital from and human capital from the poor and these will push them into poverty for the foreseeable future. Today I will review two recent papers that have bearing on this question. At the first we look at human capital and we will go back to the Ebola epidemic in Sierra Leone and we will see how school closures curtail the human capital accumulation of girls through the link of teenage pregnancies. And the second we look at physical capital and we will review the evidence on how lockdown policies affected business closures in Bangladesh. So the first paper is co-authored with the team at the World Bank and UCL and BRAC. We have Nick Buran and Marcus Goldstein at the World Bank and Imran Rasool and Andreas Muram at UCL and then Shizu Lyman and BRAC. The motivating observation behind this paper is that the pandemic or the epidemic in this case creates the need for social distancing, means that many public services, most notably schools and health provisions is restricted. The question that we ask in this paper is whether these policies will outlive the virus that motivated their adoption in the first place. And the main link that we have in mind is through investments in human capital because investments in human capital are a key channel through which pandemic can have long-term impact. Human capital investments are normally done during childhood and adolescence. So if during that time people are unable to invest in human capital, they will never recover that possibility. Notice an obvious mechanical effect of closing schools on human capital because obviously if schools are closed, people cannot go to school and hands cannot accumulate human capital. But the question of interest is whether once the schools will be open, the accumulation starts from where it stopped or whether it's permanently shifted downwards to a lower level. The key link that we have in mind and on which we provide evidence is teenage pregnancies. So the causal chain goes from school closures lowering the opportunity cost of time with men and an increase in the risk of pregnancy which will materialize as childbirth from something else and hands drop out from school. And because the effect of a pregnancy is permanent, that is once you have a child, you will have a child for the rest of your life, the effect on human capital is also permanent. That is the temporary school closure will actually make girls drop out in the long term. Our context is the, as I said, the Ebola epidemic in 2015 in Sierra Leone where all schools and most health centers closed for one year. Teenage pregnancies are very common in Sub-Saharan Africa and Sierra Leone actually scores among the highest in the continent. What we see is that teenage pregnancies are especially common among the poorest. In this graph, we give the average number of children ever born to mothers of different ages between 15 and 24 and the different lines represent different wealth quintiles. The black line is the poorest wealth quintile and the pink line is the richest. And you see that there is a strong wealth gradient in which the poorer girls are more at risk of teenage pregnancies. Interestingly, schooling seems to curtail the rates of teenage pregnancies. The girls who are complete secondary schools are much less likely to be pregnant. Obviously, this is just a correlation. It captures a two-way causation from schools to pregnancies and from pregnancies to schooling. But what we are interested in here is whether the school closures actually increase the risk of pregnancy. To answer that question, we use two key sources of variation. The first is the randomization of a program run and implemented by the NGO BRAC. The program is called ILA, which stands for Empowering Lovelyhood of Adolescents. And what the program does is that it offers safe spaces, that is clubs. And in these clubs, girls can hang out among themselves but also undertake training programs and the training are of two types. There is vocational training, which is a standard form of training that facilitates the transition between schools and labor markets. And there is also life skill training, which contains, among others, modules on contraception and relationship with men. We combine these randomized rollout with the observational variation in health center closures. Now, the reason why this is relevant in this setting is that the health centers in the absence of schools which were closed because of the epidemic, the health center is really the only place in these villages where girls can go to get a safe space and to get contraceptives and training on contraceptives. So having variation in the health center closures actually creates variation in the demand for the BRAC club. We randomized the allocation of the BRAC clubs across 200 villages and we followed 3,000 girls aged 12 to 18 over a six year period. Now, the evaluation of the ELA program was done independently of the epidemic as a matter of fact when BRAC decided to implement this program in Sierra Leone, nobody had any idea that Ebola would strike so heavily and we indeed completed our baseline survey entirely by chance a couple of weeks before the first case of Ebola was found in Sierra Leone. So we are in an ideal position because we have very rich data on time use where the girls devote time to studying, working or relationships with men, a baseline before the Ebola strikes and then again at the end of the Ebola reducing measures so when schools reopen in 2016. So we are in an ideal position and they totally by chance to study the effect of Ebola on schooling and pregnancies as well as the effect of safe spaces in on that relationship. So to begin with we show the strong correlation between Ebola, pregnancies and dropouts. These data come entirely from controlled villages that these villages in which there is no club operating and we see that in these villages the enrollment in school drops for every age. So the first figure here on the top left hand side panel shows that girls aged between 12 and 18 are less likely to be in school at every age. So if you look for instance at 13 years old before the epidemic, 90% of them were in school after the epidemic is 70%. And so of course enrollment fall with age but for every age the maroon bars which are after the epidemic are lower than the blue bars which are before the epidemic. Now interestingly for our purposes pregnancy becomes the main reason for not attending school. Here again the blue bars are the reasons before the epidemic and the maroon bars are the reasons after. Before the epidemic 50% of girls mention financial cost as the key reason for why they were not going to school but after the epidemic is 40% the most common reason becomes pregnancy. And there is a strong correlation between attending school and being pregnant. 85% of girls who get pregnant do not return to school. We can see here there's only 3% of those who get pregnant who return to school. And 73% of those who do not get pregnant return to school. There was also a policy in Sierra Leone that prevented visibly pregnant girls from attending schools. So there is a mechanical effect of this but the fact that girls did not go back to school because this is two years later so the children would have been born by then really suggest that pregnancies which happened during the Ebola epidemic actually reduced the schooling of girls permanently. Now our case also variation as I said is that too we have variation in the availability of health centers at the village level and variation in the availability of safe spaces through Ebola. The reason why we need the first source of variation is because it gives us variation in the exposure, the risk to pregnancy because where health centers are closed girls have essentially nowhere to go. So these regressions again are run in controlled villages where there are no illa clubs and we see that the time devoted to learning is lower when the health center is closed and the time devoted to socializing, especially with men is much higher. This also corresponds to an increase in the frequency of unprotected sex and the outcome of unprotected sex inevitably is pregnancy. Now the question of interest is whether there is demand for illa clubs in these villages where the health center is closed as a substitute for safe spaces and whether these illa clubs are effective at preventing pregnancies. And that's indeed what we find. The demand for clubs is much higher in villages with no health center or where the health center closed. We see here that where the health center is open we have 64% of girls attending where the health center is closed, that percentage go up to 76%. And more interestingly, it is the high ability girls one more likely to attend where the health center is closed. So this difference, this 12 percentage point difference here comes mostly from high ability girls. We measure ability baseline using Raven matrices. We see that the high ability girls are more likely to want it to attend life skill training courses, that is the courses that teach them how not to get pregnant, essentially, and less interested in the vocational training courses that is the courses that teach you how to do a job. This is in line with the fact that there are two reasons to attend the Black Clouds. One is to protect yourself from men and essentially to find a place to be safe until schools reopen so that you can go back to school when that happens. And the second reason is to learn a trade and start working. It's not surprising then that the higher ability girls seem to be interested in the former and lower ability girls are more interested in the latter. And because in some villages, there is no substitute for schooling in terms of safe spaces because the health center is closed, that's precisely the villages in which the higher ability girls are more likely to attend the Hila clubs. So now the question is whether these Hila clubs are effective at protecting the girls from pregnancies. And we find that indeed the Hila clubs undo the effect of closures. This graph report all the outcomes. This is the first on the top left hand side corner. Here is time use. So it's time devoted to learning, to work, to household chores and socializing. And the gray bars are the estimates that we already saw that is the effect of a health center's closures on these outcomes in controlled villages. And the green bars is the interaction between that effect and the presence of a Hila club. So the fact that for instance, this gray bar here is negative on time devoted to learning is what we saw earlier that girls in controlled villages where the health center closed are less likely to spend time learning and the fact that the green bar is positive. So just that this effect is undone by the presence of the Hila club. So we see that everywhere here, the green and the gray bar go in the opposite direction that is closing the health center makes girls less likely to go to school, having Hila makes them more likely to go to school. Importantly, this suspension of schooling results in a true loss of human capital. This graph here on the top right hand side measures the effect on skills in literacy and math. And we see that there is a strong depreciation of the skills in villages where the health center closed which is undone by the availability of Hila clubs. The same thing happens with pregnancies is a strong increase in pregnancies where the health center is closed which is undone by the Hila club. As we follow girls for another two years after, so 2019 and 2020, we can measure the persistence of the effects and we find that the effects are persistent in the sense that girls who were induced by the program to spend less time with men four years down the line, they're less likely to have children, they're more likely to still be in school and to have higher human capital. So in conclusions are results in the analysis of the link between human capital pregnancies and schooling in Sierra Leone suggests that there is indeed a concern, there is evidence that supports the concern that the measures which are taken to limit the spread of viruses hit young women the hardest and for the longer time because only girls of course are at risk of getting pregnant and so this creates another gender gap which adds up to the existing gaps. Beyond obvious equity concerns, efficiencies also stakes because the human capital of half a generation drops in a permanent way and this is also true for the most talented. We saw that it's precisely girls of the highest ability who are attracted to the club when the school and health centers are closed because they want to be protected and restart school when these reopen. So safe spaces are a cheap and effective solution because there's clearly demand for them but of course they have to be provided to be effective and is not clear in the context of the current pandemic the extent to which girls have a place where they can protect themselves until the school open again. So there is the real, very real risk that the talent of millions of girls in low income countries will never be put to its best use because pregnancies will essentially reduce the human capital accumulation for the rest of their lives. This has implications at the micro level for their own well-being and poverty and at the macro level for the growth of their countries who will have to do without their talent. The second example that I'm gonna discuss today has to do with physical capital. So as a reminder, the original setup was the one in which we said that poverty is essentially due to low earnings and the way of increasing earnings is either by combining labor with the human capital which we have reviewed in the case of Sierra Leone or with physical capital that is start to accumulate assets with which to run a business. This is the second of these two types of ways to increase earnings. That will be the key focus of this paper. This paper is joined with Robin Purchast at the LSC, Atia Raman and Imran Matin Abrak. So the paper relies on a rapid survey that Abrak quickly put on the field as soon as the first lockdown started in Bangladesh. And this is a survey about jobs. So he asks 7,000 households about the main source of earnings and he does so twice at the start of lockdown in April 2020 and after the end of the lockdown in June 2020. And in both cases he asks retrospectively questions about jobs and the livelihoods before the pandemic in February 2020. The samples are drawn from nationally representative surveys and they are 50% urban and 50% rural. Now, if we look at jobs across the world, we see that a distinctive feature of development is that there is a large increase in salary jobs. In low income settings, people are less likely to have salary jobs and more likely to be in either self-employment, running a small business or casual labor that is selling their labor daily without any contract or guarantee of further employment. Now, the prevalence of these three types of jobs within country also relates to wealth. So in the poorest countries, we see more casual jobs and less salary jobs and within a given country, we see more casual jobs among the poor and more salary jobs among the rich. So salary employment is rare. We see here that only 25% of our sample has a salary employment, but the percentage goes up by a good 10 percentage point for the richest people. In this graph, we have the share of people in each wealth being who do these three forms of jobs. So if you start from the lowest wealth being, you see that 40% of people are engaged in casual jobs, about 26% are engaged in small businesses and 25% are engaged in salary jobs. Once you go to the other end of the spectrum, to the richest wealth beings, you see that the patterns, the proportions are basically flipped, 35% of people are in salary jobs and only 25% of them are in casual jobs. So the question is, how does the job that you have to start with change, how does the earnings, the earnings loss due to the pandemic change depending on the job that you had a baseline and how does the pandemic actually affect the choice of jobs? So the following graph represents the coefficient of a regression of the income change between February and April. And we see that by job type of baseline. And we see that the owners of small business and casual laborers lose the most. They lose about 50% of their income, whereas salaried workers lose about 30%. So everybody loses a fair amount, but salaried workers are more protected. So the amount of loss in earnings is larger for the jobs that are normally done by the poor. Now what's more interesting is that the pandemic itself changes the occupational choice. So these are transition graphs that show for each occupational baseline what the person does in June. So this is the change in occupation from the beginning of the pandemic till the end of the first lockdown. So in these four months between February and June, there's a considerable churning. So if you start from business here at the bottom, there's only 60% of people who had a business in February they still have it in June. And 30% instead move onto casual labor. Likewise, 74% of people who had a salaried job in February still have it in June. And 30% move onto casual labor. Now the question of interest is to understand whether these transitions are permanent or they're just dictated by a standard efficiency argument that the best business survive and the worst business is closed, depends on whether the transitions are driven by the earnings in the occupation or what. So the next graph reports the average earnings of people who stay in the same occupations and those who live. For business, we see that it is those who earn the most, one more likely to stay and those who earn the least are more likely to close down. However, when it comes to casual labor, we see that it is those who earn the least, one more likely to stay and those who earn the most are more likely to transition into salary labor. Now so it's not really a comparative advantage story because otherwise we would have seen that the highest earners would have remained in their existing occupation. So not surprisingly what's going on, the true driver of these changes is wealth. So what we report in this graph is the baseline wealth of people who stay in their job. So these are the thick lines here and people who live. And you see here that it is the richest business owners who manage to hold on to their business while the poorest move to casual labor. And once you look at casual labor, you see exactly the opposite. It is the poorest casual workers who keep their casual jobs while the richest move on to salary labor. In other words, we can visualize this result as an earnings wealth frontier for those who live. So if you draw the relationship between wealth and earnings of the businesses which close down, you see that for a given level of profit, so these are the maximum earnings, the probability of survival depends on the wealth. Or in other words, wealthier business owners are more likely to be able to keep their business open for any level of profits. That means that profitable businesses of poor owners are more likely to close than equally profitable businesses of wealthy owners. The same thing applies to entry. So as business is closed, new businesses are created and the level of earnings that you need to create a business if you're poor is much higher than the level of earnings that you need to create a business if you're rich. So what's going on here is that effectively good businesses of poor owners are being replaced by less profitable businesses by rich owners. So this is a loss both for the individuals because of course I'm capable but poor owner is less likely to be able to hold on to his business and moves into casual labor, but it is also importantly a loss to society because the talent of poor owners is not put to its best use. So what we learn from the experience of COVID in Bangladesh is that the effect of COVID depends on the type of job. Casual laborers and business owners are hit the hardest. So this leads people to change jobs but the ability to change jobs that is to move towards the safer job which is salary deployment varies with wealth. It's only wealthier people who get into better jobs. This leads to inequality because again it's the wealthier people that can get the better jobs and importantly misallocation because talented poor people who would be better at business can not to be able to do so. And again gives us a reason for why the effect of the pandemic will last longer than the pandemic itself because the poor owner who has lost his business will find it much harder to restart the business once the pandemic stops. So the final answer to the question that motivated this talk and with which I started the talk, remember was is there such a thing as a big pool and is COVID a shock which is sufficiently negative to pull people back into poverty trap hence have a persistent effect on the level of poverty. Unfortunately the answer to this question seems to be yes that is there is a real danger that the effect of the pandemic on poverty will outlive the pandemic itself. And this is because the accumulation of capital both human and physical capital does needed for people to combine their labor and derive higher earnings before exiting poverty is actually curtailed by the policies which have been implemented to contain the spread of the virus. This means that if no action is taken so if no protection is given to girls for them not to get pregnant and drop out of school if no sustainment is given to wealthier less wealthy business owners who have good ideas but not enough wealth to keep their restaurants, their hair salons, their motor mechanic businesses open then we would wake up after the pandemic in a world that's much more unequal and importantly much poorer because a fact that is often overlooked once we look at poverty estimates is that poverty is not just the business of the poor but it is fundamentally the determinant of well-being in countries as a whole. Oriana, thank you so much for your presentation. It was very interesting. There are a couple of questions already in the chat and I would encourage the audience to ask more questions so you want to interact with Q&A session. I had one question on the Ebola paper and then one on the Bangladesh paper which I might just keep aside for some time. On the Ebola paper, there's a question I have on external validity. So the Ebola pandemic was different perhaps from the coronavirus pandemic because in the coronavirus pandemic we had initially in national lockdowns but then we had started having local lockdowns as countries started having more targeted approaches. The local laptops happened mostly in urban centers because that's where the transmission was the most. And so my question then is that the finding that you have with early pregnancy and school dropouts that you see in the Ebola crisis how much can we say that might happen with the coronavirus pandemic? Do you think that you could extrapolate from those results for the COVID-19 pandemic? I think you're muted. I've been doing this for two years now and I still forget. Thank you so much for the question. So I think that the results apply to any crisis that lead to school closures because the mechanism here is the lowering of the opportunity cost of time and the lack of a safe space. So to the extent that countries respond to the coronavirus by closing schools this mechanism will be applied. It's the generalizability relies on the closure of schools because that's the mechanism that we show. Thank you. Now two questions specifically on your paper and two questions are related. So let me ask them together if that's okay. The first question is from my colleague, Sam Jones who's based in Maputo. So Sam asked, could you clarify the cost of the safe space intervention per beneficiary and do you think it could be realistically scaled up? Let me ask the second question that's okay. This question is from Sarah, she's on Yana and she says, this is a very interesting presentation. What do we need to do differently to ensure that the girls receive timely protection from early pregnancies during such pandemics? How can governments scale up the BRAC model? So the basic question is about scaling up. Do you want to answer these two questions? Thank you. So the BRAC program has many components. It has the component of the safe space which is just the space that BRAC rents in the communities and the space leader was a girl of the same age who gets trained by BRAC to run the club. Then he has a livelihood training component which is the equivalent of vocational training and he has a life skill component which is advised on especially relationships with men and contraception. He also can have a financial skills and microfinance component. So the cost, so the reason why I'm giving all these details is that the main effect in this case is that of the safe space. So the safe space alone is rather cheap. We evaluated the HILA program in Uganda and there the cost of the all program was $30 per girl per year. If we remove the vocational training component which is of course important to hide the transition into the labor market but it's not so important for the safe space feature of the club then the cost is $15 per girl per year. So I think it's worth it but then again it depends on how much value you put on the teenage pregnancies and the human capital accumulation that's curtailed for these girls. Sure, thank you very much, Oriana. I have now another kind of generic question from the audience. The question is, and I'll now add my question also because this is about the Bangladesh paper. So the question that comes to the audience is that it's evident the workers in the informal sector suffer the most in the pandemic which you showed in the very clearly in the Bangladesh paper. What measures can be employed to prevent this in future? Well, let me add my specific question here which is a bit more on the mechanisms but one of the things that's unusual about this pandemic and especially in Bangladesh is microfinance institutions closed down or any kind of formal borrowing closed down. And that was mostly for those who are a lower income spreader. So as you know in Bangladesh most people who are low income say for example tend to borrow for microfinance institutions and even things like borrowing from friends and family was not possible because you couldn't move around. So how is that a mechanism that might explain your result? That for that reason because of this very specific nature of the pandemic closing down microfinance institutions and informal lending that might affect more the fact that those who are in the lower income strata could not get the credit they wanted to carry on while the wealthier businesses obviously had their own funds. And so again, two questions here. One is how informal workers, what can we do about them? Take this specific mechanism that we see might observe in your paper on the Bangladesh paper. Thanks. Of course. So there is no doubt that informality and casual labor is really the symptom of extreme poverty. So it's not just in the context of the pandemic but in general if we want to reduce poverty we need to create better jobs. In the meantime, one thing that can be done is to coordinate, to create mechanism that coordinate the informal workers because fundamentally the problem with casual labor is that the landlord which hires these people has all the power. And might these be agricultural workers or urban transport workers each in isolation don't have any protection. If they were in a formal contract they would have the protection of the law. So there is not being thought but it should be thought to organize the informal workers in a way that gives them a collective voice towards the employer. Regarding the lending, of course that doesn't make things any better because we're all in the same boat. So if before you could borrow from your family and friends now your family and friends are also affected by the crisis so borrowing has gone down and that hurts the most people with the lowest wealth. In terms of microfinance in Bangladesh I don't know if the poorest of the poor that is those who have no business can actually benefit much from microfinance because the repayments are so rapid that they don't have time to start a business but for sure the business owners will lower wealth must have suffered from the lack of credit. Right, thank you. Yeah, now go ahead. No, no, we saw in the data that precisely the relationship between wealth and closure of business was very strong during the pandemic. I mean, there's been a discussion about social protection schemes in validation elsewhere. Do you think there's a moment now for really having a much more accessible and perhaps a universal social protection because clearly targeting has become more of a problem as you showed there are salaried workers who are moving into casual labor for example. So who should you target? So do you think there's an argument for that across the board maybe not only for Bangladesh but other developing countries? Absolutely, so one result which I didn't show is that we have data on whether people receive social protection and it's all for the salaried workers. And that's why we see even the richest casual workers for instance could have been casual tutors. Casual workers don't need to be all those skills all moving to salaried employment because salaried employment is what provides the most protection. So in a way, because we can only protect the formal sector we end up protecting those who needed the least so to speak. That's links to what I was arguing before about coordinating informal workers and targeting social protection to them as well. So the social protection is not tied to the employer but rather is tied to the person. So you think that's the way to go? Yeah, and do you say any evidence of that as yet in the policy discussions? Not as much other than for universal credit but we have done some work in documentary. So the World Bank has a very detailed database on social protection programs all around the world. And you can see very clearly that the countries, the poorer the countries the worse the targeting essentially. Right, thank you. I have not two very specific questions from a member of the audience, Jacqueline Velasco, one on the Ebola paper, the other on the Baladish paper. On the Ebola paper she's asking the marital status of the girls who became pregnant. I mean, do you have a sense about are they married or not married? And does it really matter? Most of them were not. And what we find is that six years down the line when we go back in 2020, actually the program has affected the type of person that the girl married. So girls who were protected by the club end up marrying men who are more educated who are closer to them in age and who are more adverse to gender-based violence. So there is, most of the girls are not married during the intervention, but they get married later. And the intervention seems to have this effect on the quality of the man that they married. That's really interesting. That's very quite remarkable really. Let me again ask a specific question from Jacqueline on the Baladish papers. She asked the question, how does the issue of productivity enter into explaining the different trajectories of the earning wealth frontier? So how does productivity play a part in explaining the ratio should be showed in earnings and wealth? So to the extent that earnings reflect productivity, then what we have is that the most productive but poor business owners close down so that basically wealth buys you some productivity so to speak for survival. So if for a given productivity, the wealthier owner is more likely to survive, which means that for a given level of wealth, you get more productive owners surviving. So productive and poor are less likely to survive than non-productive and rich. Right. I mean, there is a very nice set of papers by Mushfiq Mubarak and co-authors on migrants in Baladish, especially as you know, Baladish has a lot of migrants overseas. And many of them had to come back when there was lockdowns in Malaysia, UAE and so on. And revenue is very important for safe employment, right? I mean, they use is as a seed capital to get started. Now, I don't, I mean, obviously no paper that's difficult to bring in because you haven't looked at that, but I'm speculating on what exactly do you see the role of the problem of migration or rather remittances being a mechanism which for financing safe employment, especially for not the poorest, but those in the lower income state, that was totally shut down during the pandemic. And this is not only true for Baladish, but many other countries which are also receive auto remittances. So this links very well to your earlier question of microfinance because remittances are another source of funding which are actually even more important than credit because they don't have to be repaid most of the time. So that's a very important source of financing that's been cut down for everybody. And contrary to microfinance which only affects the people that could have benefited from microfinance beforehand, remittances affect everybody. So no doubt there is the drying up of the funds. In addition, there is the returning migrant who's one more mouth to feed. I think it shows, you know, when we think about lockdowns, we should be careful. We only think about lockdowns in a specific country in question, but lockdowns in some other country where we have migrants from that country can also, as we saw in the case of Baladish, have huge effects in the source country. So that's something we need to keep in mind. The spillover effects of when you have lockdowns across the board in many other countries, how that can affect the country that's sending the labor. And that's something I think perhaps one needs to think a bit more about. Now there is a question here that may well be the, you know, it could be the last question because it's a kind of very broad brush question. The question is that considering the long breach and drawn out effect of the pandemic on an effect on poverty and inequality, what probable actions are required to mitigate these negative outcomes? So looking ahead to the future, I mean, that's quite difficult, perhaps, but if you wanted to just say, how do you think we could try to take particular action steps? You talked a little bit about social protection, of course, in mitigating the negative effects on poverty and inequality, if that is possible, obviously, given the situation at hand. No, absolutely. I think we're kind of sleepwalking into disaster because by not taking into account the long-term effect of a rapid increase in inequality and poverty, we will find out once eventually this will end that the problem will be much harder to solve because the longer people are left in poverty, the more the drain on the few resources that they have, the most expensive it's gonna be to pull them out. I think on the positive side, I think that all the results we have from programs that have tried to create better jobs is that the problem is not intrinsically that the people are unable or unwilling to do productive jobs that earn decent salary which keeps them out of poverty, is that these jobs are not available. So I think that any policies that maintains social protection for bad times, which is the times that we're living now, but looking in the future, policies that help infrastructure, that helps businesses create jobs is gonna be fundamental. Training programs for all the workers that have not been able to train so far. So we are now involved in a large-scale training evaluation in Bangladesh with BRAC and there is huge demand for training. But of course, given the positive funding, people cannot afford it. So funding training or even giving loans for workers to be trained is gonna be a very important step in this. And so that's all physical capital. What about human capital? What can one do on human capital? Because as this is software, your Ebola paper, there's significant scarring of human capital, especially for girls. What can do about human capital? So training is more on the human capital side, but that the human capital problem is actually not just a low income country problem. It's a problem everywhere because the pandemic has interrupted the accumulation of human capital among low socioeconomic status children everywhere. So there's no doubt that we need to take, we need the election now. And it's very, very cheap to provide human capital for children. But if we don't do it now, fixing it later would be much more expensive. So especially children at the primary levels, primary schooling level, that's the skills are developed the most. So, yeah, absolutely. There's a very specific question here. And I think we still have three minutes here that comes from Annie Boyrwood who's asking, and this is about the serial on paper again, how would the men's, how husband's attitudes to a gender-based violence measured with the men involved in the research as well? Very specific question. Yeah, yeah, yeah. So actually in the first two rounds of data collection, we only interviewed girls. And in the last round, in the 1920 round, once girls were forming stable partnerships, we thought it'd be important to interview the men as well. And so we did. So we interviewed both the girls and their partners. And so the gender-based violence measures are collected and interviews with the partner. Right, thank you very much. That's a very clear answer. I'm going to stop here because we are in a situation of an online conference where we need to move to the next session in a couple of minutes. And the four-parallel session is going to start in two minutes time. So do make sure that you get to the one that you want. Orina, thank you so much for a very clear presentation, very true, very fascinating papers, and also a very excellent Q&A session. I think it was really very insightful and looking forward to having you again engaging with you in the future. Thank you. Thank you for inviting me and thank you for the questions. Thanks.