 Hello everyone and welcome to this public webinar on the lasting consequences of COVID-19. And so this is a joint event. My name is Ingele Alder, by the way, and I'm here to introduce today's speakers and to tell you about how this event will proceed. So it's a joint event organized by the Toulouse School of Economics and the Institute for Advanced Study in Toulouse. So the Toulouse School of Economics is a research center and education center in economics at the University of Toulouse. And the Institute for Advanced Study is an interdisciplinary research center hosted by the Toulouse School of Economics. Today's speakers are both professors in economics at TSE and also actively involved in the IST. Before I introduce our first speaker, I would like, however, to tell you how this hour will be structured. So each speaker will talk for 15 minutes and after each 15-minute talk, there will be a five-minute window for questions on the talk. Those questions will be in writing only. At the end, after the two 15-minute talks plus the five-minute Q&A time, at the end there will be ample time for further questions and in the end, hopefully, there will also be some time for questions that you may ask orally. But let me tell you how you can ask questions in the meantime. So those of you who are here with us on Zoom, you may ask questions in the Q&A window. Those of you who are following this on Facebook can ask questions in the comments to the video and those comments will then be copied and pasted into the Q&A in the Zoom. And finally, you can also ask questions on Twitter by using the hashtag, hashtag TSC debate. And by the way, those of you who are here with us today on Zoom, I invite you to have the Q&A window open. So even if you're not asking questions, you can like questions and so that the most popular questions will automatically come towards the top and will be asked in preferentially in that order. Okay. So also, actually, apart from questions, both of our speakers today will ask you all to provide some input and they will tell you exactly how to do this. But just to let you know, give your heads up, they will ask you to log into a website called menti.com. So that's M-E-N-T-I.com and they will ask you some questions and you will see the results of these polls that they will thus hold. We'll see the results of the polls in real time on the slides. Okay. So let's start now the presentation. The first speaker is Victor Gay. So Victor is an economics professor at TEC who specializes on the role of historical institutions and events for long-run economic growth. And Victor will talk about the economic impact of COVID-19. The floor is yours, Victor. Thank you very much, Nguyen Nga, for this introduction. Can you hear me correctly? Okay. So let me share my screen for my presentation. Okay. Okay. Can you see my screen correctly? Okay. So let me start. So during my intervention, I'm going to talk about the economic impact of COVID-19 and more precisely from a historical perspective. So without any doubt, I think that we can say that we're living today the worst health and economic crisis of the 21st century. So as of today, we have about 1.3 million confirmed deaths from COVID-19 and about 55 million confirmed cases here. And the main reason for that is that we have no natural immunity to this disease and there is no cure yet. So there are some vaccines, as we heard last week, that are near to completion but are not available. And so this has generated two types of crisis. So first was a health crisis, which government tried to stop using large-scale non-farmaceutical interventions, but this in turn degenerated into a large economic downturn due to those interventions. And that's kind of a unique feature of the crisis where it's the combination of these two phenomena. So during this short presentation, I'm going to try to provide you a historical perspective about what past historical events can teach us about likely long-term consequences of this crisis here. So we can attack this from two angles. The health crisis angle and the economic crisis angle. So I'm going to start with the health crisis angle here. And so the idea is to try to find some commonalities with past epidemics here. So this plot shows you reproduction rates and case fatalities rates for past epidemics. So reproduction rates are really hardly expected. A number of cases directly generated by one case in a population that has no immunity here. So you can see that the graph in the graphic, it ranges from 0 to 20. So those are estimates. So there is a range of uncertainty around them, but they provide some form of order of magnitude. And you can see it goes from the deviant flu, which has relatively low reproduction rates to measles, which has a relatively high reproduction rate. And the case fatalities rate is the number of confirmed death divided by the number of confirmed cases here. And you will see also a wide range here. So we're still, the epidemiological crisis is to be currently developing. But what we think now about COVID-19 is that we're somewhere in the yellow square here. So there is some uncertainty around this. Maybe a bit more on the right or a bit more on top. But broadly speaking, we should be in this area here. And as you can see, one of the more common comparable disease that we have with COVID-19, especially in terms of types of symptoms that you have is the influenza of 1918. So I'm going to try to draw some historical lessons from this case here. So what can we say about the influenza epidemic of 1918? So it was a relatively brief pandemic, which last talked about for two years. But something that is also common with the current crisis, it's a worldwide diffusion. So it's unclear from where it originated, perhaps from the United States. But what's clear is that it propagated pretty fast all over the world. And here those are against some estimates, but it is estimated that between 20 and 50 million people died during this crisis here. And also in common with today, there were some non-pharmaceutical interventions. For instance, in the US, with social distancing measures and lockdowns, partial lockdowns, but there were rather limited here. So what can we draw from this case? Here I'm going to focus on the case of the US and what researchers in economic history have done on this case. The reason is that you have better data on the US case in terms of disease and mortality measures. And also very importantly, this coincided at the time of the end of World War One. And so for this reason, it's extremely difficult to assess the consequences of this epidemic in continental Europe because you had World War One on the soil of continental Europe here. So that's why I'm going to focus on the US case. Also the magnitude of the disease of the epidemic in the US was rather large from 600,000 to 700,000 deaths from this disease in 1998. So the first thing you might ask about the consequence of this health crisis is which populations were the most sensitive to this crisis? And here is a question I would like to ask you before I show you what researchers have to say on this. So one question is, what were the most important determinants of the excess mortality across US cities in 1919? And so one question is, were those populations that were had the worst base of unhealth more susceptible to the disease? Or was it about something about income? Or was it about proximity to military bases which some people think that might have contributed to the diffusion of this disease? Or was it some environmental quality, baseline and air quality? So here I'm going to ask you, as Ingéna said, to go on www.match.com. You just have to enter this URL on your browser and just enter the code that's on the screen here, 8410642. And you can choose and we can see what people think about that. And those results should theoretically appear. Surprisingly, a very low number of people think baseline air quality was important. So let me show you what researchers think about that. Let me try to share my screen from, so where is it? Okay. Can you see my screen? Yes, okay. Sorry about that. Okay. So here is what researchers have found. Okay. So let me explain what it is. So in a very interesting recent study, Clay Lewis and Severinini, gathered information from 400 or so U.S. cities, the larger ones, and gathered information on excess mortality from the pandemic from this disease and various city-level characteristics. And they run what's called a multivariate regression. But essentially what they do is to compare excess mortality rates across cities that have different characteristics. So to make results kind of easy to interpret, they divide cities in third size between characteristics. So cities with high, medium and low informed mortality rates before the pandemic, high, medium and low literacy rates before the pandemic, et cetera. So what you see here are estimates that compare high versus low and medium versus low. Excess mortality rates across these characteristics, once everything, all the other, all the other correlates are taken into account in part of that. Okay. So this is the end of the correlation. Victor, can I just ask, there's a clarification question. Can you just define what baseline, what you mean by baseline? Oh, sorry, baseline is before the pandemic itself. Sorry. Yes. So do places that were initially, that had higher infant mortality rates initially, which means that they had worse health conditions, worse health environment, where they're more, can you observe more mortality from the disease in those places later on? That's the idea. The idea is that you measure those characteristics before the pandemic ends. So what you see as people, what kind of right is, the most two important determinants are, appear to be pre-pandemic health conditions and pre-pandemic illiteracy share. So there is a limited number of data available here. So infant mortality is some approximation for the health environment and illiteracy rates is an approximation for income levels. You can see that cities that were under a high group in terms of infant mortality had about 20 or so more deaths per 10,000 persons compared to the low group here. And for medium, for cities in the medium range, it was about eight or nine relative to the low group. And you can see that income and health, those places that characteristics are about equal contributors to excess mortality. However, once those are taken into account, there is a very limited evidence for, for a role of the proximity of military bases. Also, it appears that the environmental quality was not that much correlated with excess mortality. So from there, what we can get, what we can get is some policy recommendations on which populations to target, both to prevent the spread of the epidemic and also which population that were likely more affected, although this might not appear in the statistics. Yes. So here overall, so what researchers have found is that the influenza of 1918, 1919 aggravated pre-pandemic social epidemic disparities. So this is something important that we can draw from history and we can really, we can really have some clear policy recommendations here, I think. All right. So now what about the very long run effects here? And so there are a series of very interesting studies that try to assess whether being born during the pandemic in 1918 dramatically affected your long run life path. So Dugerman has a very interesting study in 2006 where he kind of implements, try to test this hypothesis. And so here it's a version of that hypothesis where you take people in 1970, so who are 50 years old, about 50 years old in the US census and you compare some of their characteristics depending on their quarter of birth here. And so here you have these plots, which represent the share of this population that is disabled in 1917 when they are 50 years old as a function of their quarter of birth here. And the quarter Q4, Q3 corresponds to the time where these people were in utero during the pandemic. And so people who are about here who were in utero earlier or after these points were not subject, it was not during the epidemic. And what you can see directly is that for first you have a downward trend in the share of people who are disabled over time. It just means that a level of economic prosperity is just growing and people are getting better over time if you're born a dictator. But what you can see is that there is a deviation from the trend for those people here. This deviation might look a bit small because it's just 1% at each point compared to the long-run trend here. But however, it's a really long time after, so it's about 50 years later on. So it really means that you have long-run scaring effects. And you can observe that on a wide range of characteristics, but I just showed you three. So this is a more direct health measure. But you can also see that reflected in the probability of dropping out of high school. So those people who were in utero during the French epidemic in the United States were more likely not to graduate from high school. And overall, this also reflects in income levels. So these people are slightly more likely to be below the poverty level. So all this to say that these epidemics that we're living might have some very long-run health and economic consequences, scaring effects on people. So this was to discuss about to provide some historical perspective on the health crisis. But we saw that what is specific about the current situation is the combination of the health and the economic crisis that was generated by those non-pharmaceutical interventions. And so here we have to think about the economic crisis. So the thing that comes to mind directly to an economic historian is the Great Depression, which was until now the largest economic doctrine that we observed in the 20th century. And so it started in 1929 in the U.S. and broadly speaking it lasted until the early 1940s and World War II. And here I'm going to ask the same question. Can you observe some scaring effects? But I'm going to take a different angle, which is the angle of, is there a scaring effect when you enter the labor force during this crisis? And this is especially interesting to us professors and teachers because our students are going to go on the labor force pretty soon. So here what's difficult is to kind of separate those effects that are common to everyone that is on the labor market at the time from people who are just entering here. So these kind of age effects. So just to show you, those are some figures from Moulton was an interesting study on that. These two figures show you discontinuity in labor market conditions before and after the crisis of 1918. On the left hand side you have a graph that shows you employment in the manufacturing sector here. And you can see that employment is broadly stable somehow growing before 1930. But then there is a shock dropped right after the crisis. And then it takes about one decade to recover the pre-financy status. On the right hand side, it's more about income and wages in the manufacturing sector. And you can see that wages were growing rapidly before the great recession in the United States. And then you have an enormous drop here and by 1940 you have barely recovered the levels you had in 1921. So once you take this into account, and you partial them out, what you can do is try to compare people who entered the labor market right before 1930 versus right after 1930. And in the United States at the time it corresponded more or less to people who were born around 1916. The reason is that at the time it was legal to enter into the labor force at the end of grade 8, which corresponded to the age of 14 and most American people were actually doing that. And so if you focus on those people who left school at age 14 and had no other opportunities to continue education, and if you compare like before and after, those people born right before 1916 who presumably entered the labor force right before the great recession to people who were born right after 1916 and presumably entered the labor force during the recession and you look at their income long after. So here you look at their income in 1940, so 10 years after the crisis. You can see that you have discrepancy. However it only occurs in those states. So high shock means it's the states that were the most severely hit by the economic crisis and low shock is the other half of the distribution of the states that were not that much affected by the economic crisis. So you see that nothing much, there is not much difference in those places that were not really affected by the crisis, but in those states that were most affected, you can see that 10 years later people who were born right after 1916 had much lower income than those people born right before 1916. And you also have evidence on that about the great recession that happened in 2008. So you have some kind of long lasting effects that can last for decades after the shock. So overall I hope I convinced you that history can provide somehow useful data for policy in the current situation and you can see that to summarize low socioeconomic status individuals really bear this proportionate burden of the health crisis and also labor market entrance here and we have a lot of evidence of long lasting staring effects. But of course we need to be really modest about what history can tell us because of the original feature of COVID-19 which is the combination of these two crisis which didn't really happen in the past. Moreover there are a lot of questions that we need to answer which is how those two features will interact. How will health and the economic crisis might generate in itself another health crisis. And also some original features that we didn't observe in the past for young children due to school closures and also the consequences for children in quality especially for working mothers. Thank you very much and now we have a few minutes for questions if you want. Thank you very much Victor. Yes, so we have five minutes of questions now. So one question is that you talked about in utero effects and the question here is whether this is possibly perhaps instead related to the death of parents rather than in utero effects. School, sorry. Whether, so let me come back to the relevance of this here. So here in this study you take into account, because those are in utero, I don't think it's driven by mothers who died during childbirth although this is clarified. But I think it has been shown that you might have some selection potentially on who gave birth at this time. Here it's not really related to the health of the parents. I think it's more related to the military, World War I military draft. So you had this argument that all these results were driven by parents, so fathers who are not in the U.S. who were in Europe waging war during the time the epidemic was there because of this selection the pool of people who remained, the pool of parents who remained in the U.S. had lower social economy stages than soldiers who are truly left. It has been shown by several studies that there is a little bit of dissection effects from the characteristics of the parents of the fathers who didn't go to war, but it's extremely small and cannot really account for this. So sure, to answer so surely, but it's not, it cannot really account for the larger results here. Another question is about using infant mortality as a measure of poverty. Is that something that you have seen? Yes, so here they're using, in this study there is an infant mortality to measure for an approximation of the health environment. The share of illiterate people is used as a more close approximation of the income of the city in question. So of course it is far from perfect. I'm not saying it's perfect, but one issue that you have in economic history is the information at the city level in the US at the time. For this time, it's not widely available. So basically you can use some sources, you can use what you have. So I think it's more because you lack information on purely income and you lack information on other health measures that you use that here. But I agree those measures are somehow debatable. Okay, thank you. Another question is about the graph that you showed about those who had reached the age of 50. And the question is how do you deal with those who died prior to reaching that age? That's a very valid point. So here what you see is those people who were actually alive in 1970 were alive at age 50. And as you can see, it's fairly obvious here. So I like those graph because those are raw data. So I mean, those are averages with those, with a line that represents the long run trend. And so you can really see what's going on. But of course, and that's a very valid point. There is a selection bias. If, I mean, it's obvious from the graph, people who are more likely to be disabled are more likely to die before the age of 50. So here we have a selection. And so actually what you can think of is that if anything, this deviation from the trend here is underestimated compared to the real deviation if those people didn't die, so to speak. So I think that's a valid point. But if anything, it kind of, this plot kind of underestimates the true scaring effects of the bottlenecks. Okay. And then there was a question about the fatality rate for syphilis that was on your, one of your graphs. Okay. And so is this considered untreated syphilis? Because nowadays the case fatality should be very low. Yes. Here, I'm not entirely sure. If I had to guess is that those numbers are from the syphilis epidemics in the 19th century. Those are numbers from historical data. But I cannot be sure. Yeah. My apologies. Thank you very much. Victor. Yeah. It's time for. Astrid happens it's to make her presentation. So Astrid is also an economics professor at TC and actively involved at IST. Her research focuses on the influence of emotions and other psychological traits on economic decision making. And her presentation is. Yeah. Yeah. Okay. So I'll try to share my screen. Does this work? Yes. Yeah. Yeah. And I can see you. You can hear me. You can see me. I'll try. Okay. So thank you very, thank you very much in your life for this introduction. Thank you everybody for being here. So my name is Astrid Hoppensitz. I'm a researcher in experimental and behavioral economics. At the Tudor School of Economics. And I'm also a member of the IST. So then I wanted to talk about a little bit doing this. 15 minutes about how is this COVID pandemic has been influencing how we interact with each other. And so I'm going to start with. In social interactions. So. So for, for, for discussing that. One thing that we first have to think about is how this COVID. A pandemic has been affecting us in general. And so first and foremost, obviously there is this novel disease COVID. That we are still. Largely discovering what the effects are, what the health consequences are of it. But even besides the immediate health consequences, one of the big problems of this pandemic is obviously that it's spreading so fast. So that's also why there are so many of these interventions. And talking about psychological effects of, of this COVID pandemic. Obviously one of the things that I wanted to talk about is talking about psychological effects of, of this COVID pandemic. Obviously one of the first problems is that those that have very mild symptoms, for example, or even no symptoms might very well spread this disease to others. And then afterwards feel guilty or shame about that. And so this obviously is a problem that we, we have to deal with and maybe we have to take into account concerning the long-term psychological consequences. But I don't want to focus so much on the direct effects, effects of those that catch the disease, but more on the general effects that this COVID pandemic has been having on how we interact around the world these days. And one of the consequences obviously is that this pandemic has been introducing new worries concerning economics concerning health. And one thing that we know from psychological research and research in economics is that women are in generally much more risk-averse. So women react very differently with respect to risk in general. And this is also something that we observed very early on doing this pandemic that there is a gender difference in how people react to this pandemic. So women are generally much more likely to react to the health recommendations are more likely to wear masks, to isolate, to wash their hands and so on. So this in addition was the things that we know concerning the economic consequences of the pandemic, namely that many people have been losing their jobs. And this is also correlated with sectors which are correlated with gender has been leading to a very significant gender impact. So all around the world women have been pushed more into traditional gender roles. They've been staying at home more taking care of children or taking care of the homeschooling that obviously became important in the last months. So there has been this general worry. And this worry is not only there for adults, but obviously also there for children. And this is also something that the researcher has been starting to look into during the last month, asking children from different countries how they've been experiencing this health crisis. And one thing that becomes apparent that they are very worried that they've been experiencing depressive symptoms that they've been suffering a lot also from the isolation, not seeing their friends, not going to school and so on. And one thing that in general also comes out what helps all of these is more information. So basically reducing the uncertainties, making it more clear how you can protect yourself, how you can protect others. What are actually the consequences is something that helps as well adults but also children to deal with these uncertainties and these worries. So the third point is that obviously due to all of these problems people have been starting to isolate during this pandemic all around the world, staying at home for extended periods of time. And also from questionnaires that psychologists have been doing now for different countries, we see that people report much more depressive symptoms due to that. And this might be due to different causes. So for some it might be because they feel isolated and lonely. For others it might be because they're exposed to increased stress because they have to stay at homeless in crowded housing maybe or with people that they have difficult relationships with. And at the same time this is obviously important across the lifespan. So it's important for young people, for students for example, that live in very small apartments and then in addition got blamed very often for the spread of the virus because people started to talk about these parties. And it's also very important for the old that are very often identified as high risk groups and were isolated for extended time periods and that suffered due to that. So in the beginning of the pandemic people said like oh this isolation might be actually a good thing for people that are rather introvert because introverts feel more comfortable staying at home and not having too much contact with others. But actually from the questionnaires that have been done over the last months it comes out that extroverts actually deal much better with these kind of challenging situations. And that this is mainly due to the fact that extroverts usually are much more optimistic and have a more optimistic outlook on life in general and that they are also more motivated even during such challenging times to try to find alternative ways to stay in contact with their social circles. And fourth, and this is the point I want to talk about today mainly is that actually our everyday interactions have changed a lot during these last months. We've been staying at home a lot. We've been moving a lot of interactions online. We've been doing video calls, video conferences, teaching on Zoom and so on. And even if we meet people face to face in the street for example these interactions are modified because most people now wear face masks and we are instructed to keep social distances so we hug much less we touch each other much less a lot of these things have changed. So let me talk a little bit about what's actually happening when we usually interact with others. Pre-COVID times so to speak when we meet people. One of the most important things actually when we meet others is usually that we look in their faces. And faces are actually psychologically really really important for humans which is evidenced by the fact that even a few hour old babies already can see or detect a human face and focus on this face. And even adults if you see for example an advertising as a human in it then usually you focus on the face first. So if we see a face we see obviously lots of things in these faces we see the expression we see the characteristics of this person. But also one thing that is very important when we see people is we see where they look. So this also is something very human. We have the white around our eyes which enables us actually to detect where the focus of someone is. And this is actually very important for example when I want to teach something. So if I want to teach someone how to use a certain object I will keep switching my own attention between looking at this object and looking at this person. And I will teach to see whether this person is actually looking at the object that I'm at myself or at someone else. And this kind of joint attention so the fact that I know that you look at the same thing and that you know that I also look at the same thing is actually very, very important for effective teaching because only that way we can be sure that our message gets across. Now so if we talk actually communication is usually not an abstract conversation. But it's a constant back and forth parallel dance of lots of little signals that get sent back and forth. So people are nodding, they are giving little sounds like they are laughing. And all of this is very important for successful communication because it sends signals to the person that speaking that their message is well received. It also enables the listener to send these attention getters so I want to say something, I do not agree or I didn't understand something that enables a successful communication. Now obviously during these COVID times we are interacting now increasingly through screens and we might wonder what is all changing there. So obviously when I see someone on a screen I might see where they are looking but actually this is not really informative so if someone looks away on a screen from me this doesn't tell me that this person is not focusing on me it might be just because the camera and the screen are maybe badly aligned. Also during an online conversation usually we don't get these small signals laughter, these sounds this might be rather either because microphones are muted there might be a bad connection and even if I get the signal but there are multiple people involved in the communication usually it's very hard to see who sends me the signal because all of these signals come from the computer to myself so I cannot detect who is sending me the signals. So this is why these interactions on the screens are very often so frustrating for speakers because they do not know whether their message gets across and why they are also very often so frustrating for the audience because they cannot intervene as quickly or spontaneously as they would naturally. Now one other thing that we do when we usually interact with people is we decide whether we want to trust these people or not and trust is enormously important for any kind of economic interactions so this is just a graph that shows you the scatterplot between GDP per capita for different countries and average answers to a general trust question so asking how much do you think in general can people be trusted and this is a result often observed is that there is a positive correlation between the two and this is just one evidence that actually if we for any kind of economic interaction we actually need some kind of trust we need a trust that money is worth something we need trust that the seller is giving us the product we need the trust that the buyer pays and so on so if we meet someone and we have to decide whether we want to trust this person what do we do so one way to decide whether we want to trust someone is obviously to use our own previous experience to as a signal for that or we might use reputation for example by third parties telling us either directly or your rating system for example online whether this is a trustworthy interaction partner but in many situations we do not have any of these informations and one thing that we base our self a lot when we need to decide whether we want to trust someone or not is the face so how do we decide whether we want to trust someone so actually there is one very easy way how you can make yourself look more trustworthy it's simply by smiling so here you have two pictures of a young woman one she looks slightly grumpy on the other one she is smiling and you notice that on the smiling picture she looks much more attractive but also people consider this person to look much more trustworthy on the smiling picture so this you see for example these kind of scatter plots these are the same person that took a happy picture and an angry looking picture and then people rated the trust of this person and you see things are usually higher for the happy photograph so actually in a normal face-to-face interaction if I see someone smiling or being in a good mood then this is actually informative because it tells me something about how this person might react with respect to myself however if online I'm doing an interaction with someone and I see just a smiling photograph this is not really informative for myself because this photograph doesn't mean that the person is happy or being in a good mood right now and so this for example is linked to one of our recent studies where we were wondering whether actually people use this kind of signals in a strategic way so I'm an experimental economist so for this we invited participants to come to a laboratory environment where they played the roles of either what we call a manager or an employees and the manager had a very abstract task that he had to give to one of these two employees and we varied basically whether this task was either a desirable task so a task that you wanted to get or an undesirable task so a task that you would rather not want to get so these people were anonymous they didn't know each other beforehand but what these employees must they could take a photograph of themselves and send this photograph to the manager before the manager took their decision now we looked at these photographs and we basically analyzed how much people smile on these photographs and so these are results from two studies so on the first experiment you see that when the task that people are competing for is basically something attractive a desirable task they're smiling much more so they're displaying much more positive valence then if they're competing for basically something that they would rather not want to have as a task so basically people understand the strategic nature of smiling or to expressing certain kind of emotions and so this means that our everyday interactions right now that inhibit these kind of signals really are changed so obviously even doing the COVID pandemic we're not only interacting through screens we also have face-to-face interactions however the strange thing doing these face-to-face interactions actually is that we usually do not really see the face of people because most people nowadays wear masks and so this is linked to another ongoing study that we're currently doing right now where we're interested in the question whether people actually can detect the facial expressions of people that wear face masks whether they can actually detect smiles of people that wear masks and so for that I would like to invite you to do actually a very short oops a very short question again on mentee as Victor has done it in the first part so you can go to www.mentee.com use the code that's shown there on the top of the screen and actually my question is on which of these two pictures the same person in this case obviously is the person smiling and I'll let you a little bit of time to look at it respond to it okay you know answers should come in I'll wait for two more seconds okay so I don't know if other people are responding but actually from this you already see it's actually a very clear vote for the left picture and it's true on the left picture since this is actually one of my colleagues is smiling however what's the strange thing is if you look at the photographs there are actually only very very tiny differences almost imperceptible differences between these two photographs and still most people have actually an intuition on which of these two photographs someone is smiling so if you see the same person this is actually kind of easy because you see them side to side you can actually really compare it it becomes a bit more difficult if you just see the person once and so this is actually one thing that we are currently investigating oops here we go this is something we are currently investigating so if you're interested I invite you to participate in our survey that's currently ongoing so this is the link to it so it's a TSC minus fr.eu slash suym so smile under your mask it's a very short survey that takes maybe 10 minutes and with this survey I hope we can study how people are interacting even doing this kind of strange modified social interaction during this pandemic so thank you very much thank you very much Astrid so there is one question here in the Q&A window so the meaning of trust is very context specific how is trust measured in cross country comparisons so the the data that I showed you from this graph this is basically I can show you that again so this is basically a question that comes from the world value survey which is a survey that's used across lots of different countries and so on so I don't say this is the best way to measure trust but it's basically a very general question that just says how much do you agree with the statement most people can be trusted so it's not the question is not context specific so it doesn't give any context it just leaves it open but indeed it depends on the context yeah thank you another question is so in the comparison that you show there between these two pictures you had one smiling and the other one not smiling the question is whether you also look at what people answer if you just show one picture and you ask is the person smiling or not smiling yes so indeed so this is one of the things we're actually interested in I made it a little bit easy here maybe by giving the two pictures side by side because we really compare it but actually what we want to see is how people just react to seeing a person without having such a comparison because yes indeed you don't see the person twice in front of you and you can make the comparison which of the two is the smiling one but you have to decide kind of spontaneously is this person smiling or not but what you can see basically there are lots of muscles that we have around their eyes and these kind of muscles are very much active when we are smiling and they're actually surprisingly good in detecting these kinds of muscle activities to detect if someone is smiling or not okay thank you so in terms of questions here in the Q&A window I don't see any more questions so I think we'll now open the floor for a general Q&A to both speakers and should I stop sharing yeah that would be good I think if you stop sharing those slides and Caroline yes so we we don't have any questions right now no questions okay so the size will be available on our website tsc.fr.eu and you can see the video on our Facebook page or we'll post it on YouTube if there are no other questions if anyone wants to ask a question you have to put your hand up sorry maybe so now you can raise your hand you can ask I see a question by Immanuel Bomsen Immanuel Immanuel you have your microphone off I think now it's on yes hi thank you very much for both very interesting talks both speakers I have a question to Victor concerning excess mortality as the consequence of the austerity measures after the economic crisis 2008 I think of countries like Greece Portugal is there any evidence that these austerity measures influenced mortality yes definitely so there is a stream of research on these topics the health consequences of economic crisis and indeed there is for the for the great recession and crisis in Greece in particular I don't have the studies in mind exactly but there is evidence that I mean when people lose their income and their jobs the detrimental to their health in general now I don't know if this topic has been explored in that great details for the very long and scaring effects about for the great recession but that's I think that's a definitely valid hypothesis to explore and this is where when I discussed about the interactions between the types of crisis this is certainly an application of this idea okay so may I may ask just as a follow-up is it is there a chance to get these results or some fair comparison let's me phrase like this some unbiased comparison between the crisis in the 1930s and the crisis in subprime crisis and follow-up crisis because these are long-term as you said long-term effects and we are basically just too close to the other is there any method to rescale the effect so that we can do a comparison so now that's a very interesting point before I answer exactly to that in general whenever you do when you want to draw some inference from historical cases there is always a trade-off if you want to put that in line with the current situation the advantage of history is that if you have a very long-run perspective but the main issue is that the more far back in time you go the less comparable things are so the advantage of the Great Recession is that you can look at some long-run consequences in the 1980s when people are 60 or 70 years old something that you cannot do with the Great Recession today but of course the context of the time and the margin is affected were very different so here to answer your question there is no magic method it's about understanding if you want to draw this type of comparison you have to understand well how both crises kind of affected incentives and choices and affected people to understand how whether these comparisons are valid or not so here it's a bit like every time in research you don't have definite answers it raises more questions I would think that to be really careful when you draw this comparison to some extent those are somehow valid but you have to be a bit careful for sure. Thank you Victor so there is one question here I think to Astrid which is what research has been carried out in societies where masks are already a normal part of life? That's a good question so actually it's mainly Asian countries that are that have this tradition of wearing masks already in everyday life and I mean just concerning one-to-one interactions it's a little bit complicated because Asian culture is very different with respect to many other things already so it's kind of difficult to compare these kind of groups and conclude that it's due to the fact that these countries have been wearing masks just one thing so for example this is as I say it's difficult so people from Asian countries are usually much more focused on eyes to determine whether someone is smiling or not but this might also be because they are just very often they have less this habit of making this extreme smile of mouse expression basically that you have very often in Europe or in North America and in Asia basically the smile goes much more through the eyes but I don't say that this is because of the masks it's just Thank you so just one last question but it's to both of you could each one of you say what you think is going to be the most lasting impact of COVID overall in your respective research drawings Victor, you want to go first? I mean there are so many dimensions it's hard to to focus on one so if I had to think of one that comes to mind right away it's the scarring effect of entering the label force during your research and that's a very difficult problem to solve because you have some crowding effects so now that people can shift a bit their entry in the label force this has negative externalities on other cohorts of people and so it's always difficult whenever you want to have a welfare analysis it's unclear whether you have to so called sacrifice one cohort and then the others are coming carrying on as before is it better to kind of wait and kind of spread out the negative effects on everyone so that's one thing and the other thing on that is that we saw with a great recession that it had very long lasting consequences and that's what I'm worried about I think and that's why I think we can have somehow clear policy recommendations for governments to target policies toward the people who are right now between 18 and 25 that's important okay so from my point of view so regarding just interactions I think one interesting thing and maybe let's finish on a positive note is that I mean for decades people have been saying oh we have all these tools for online interactions video communication is nothing new but it never really took off people never really used it because it always seemed difficult and it always seemed cumbersome but people really never really tried to find solutions to it or to get over it and now the kind of interesting experiment that happened is that over months companies and individuals were forced just to communicate like that and of course you can in the beginning people were just oh we'll just deal with it but now since it keeps going on and we also we don't even know how soon it will be over people also try to find better solutions how to make it better so to make interactions more natural and more productive and I think the long-term consequences of that might be that maybe some of this will stick around namely those tools that have proven to be effective tools and in the other cases we actually we learned actually why they're not so effective so I hope that maybe one of the positive effects of this crisis will be that we'll be able to maybe use these tools in a better way reduce maybe travels in some ways and communicate more effectively well thank you very much to both of you thank you and I think that there are quite a few questions still but we don't unfortunately we don't have time for those but thank you so much to all of you have attended as well and just Carolina saying the video will be viewable and the slides will be made available and we also welcome you to check out the website of TEC for upcoming webinars on the consequences of covid and other societal societal issues thank you very much well and stay safe thank you very much