 So, the floor is all yours, Antonella, for your paper that you did jointly together with Marc Gertler and Christopher Huckfeldt on temporary layoffs, loss of recall and cyclical unemployment dynamics. Thank you so much. So, thank you so much for having me in the program. It's really a great pleasure, also a great pleasure to be the second after this very, very interesting paper. So, this is different topic, so it's going to be a talk about temporary layoffs, loss of recall, which I'm going to define, and cyclical unemployment dynamics, and this is, as has been said, John work with Marc Gertler and Chris Huckfeldt, who just joined the Federal Reserve Board. So, in this paper, what we basically do is to both measure and models temporary layoffs and the role that temporary layoffs play for cyclical unemployment dynamics. And we are motivated, as you can imagine, by the unprecedented increase, it's going to be a paper about the US, by the unprecedented increase, and it's very different in the euro era in temporary layoffs in the recent recessionary episodes. Essentially, about 15% of the workforce of employed workers was moved to temporary layoffs at the onset of the COVID crisis. Now, at the same time, because this recent recession is very unusual feature, we also want to look at some earlier evidence. So we want to think about the framework that can capture recent data, but also historical episodes so that the framework can also be used potentially for future, to study future episodes. So what we do, we first document the contribution of temporary layoffs to unemployment dynamics starting in 1978. So we go back to 1978. So let me introduce a few definitions. So first of all, ex ante and ex pause layoffs can be both temporary or permanent. And actually, many workers have some expectations, that's how temporary layoffs are measured, that they will go back to their previous job. And actually, many of those workers with such expectations, they actually go back to their previous job. Because workers in temporary layoff have a high recall rate, high re-employment probabilities relative to workers in permanent unemployment, this flow from employment to temporary layoff is typically perceived as a flow that moderates cyclical unemployment dynamics. That's the traditional view. What we're going to emphasize instead is a different factor, and in particular, related to the fact that those workers who are on temporary layoff, who exit unemployment to temporary layoff, may lose attachment, may lose connection to the job, to the previous employer over time. And a phenomenon that we're going to call loss of recall, loss of the recall option. In this case, what happened is that layoff, where ex ante expected to be temporary, become ex pause permanent, and in particular, they inherit the lower re-employment probability of permanent layoff workers. And so we're going to argue that this second factor is instead playing a destabilizing role for cyclical unemployment dynamics. So what we do, we develop then, finally, a model of unemployment fluctuations that is going to distinguish between endogenous layoff that are both temporary and permanent, as well as endogenous flows between the three labor market states that we're going to have in the model, employment, and two different types of unemployment, jobless unemployment. So these are going to be, we adopt this terminology that has been introduced recently by Holland Kutliak, jobless unemployment and temporary layoff unemployment. So jobless unemployment are workers who have some expectations to go back to their previous job. Sorry, they have no expectation they're jobless to go back to their previous job, and so they search for a new job. And temporary layoff unemployment are workers who have some expectation to be recalled back, and so they wait for recall. And a particular important flow is going to be the one between these two states, between temporary layoff unemployment and jobless unemployment. Then we calibrate the model to pre-pandemic data, and we show that the model does well in replicating some business cycle facts. And finally, we turn the attention to the COVID-19 labor market. So while we do that, I already mentioned that, so let me be quickly on these slides. We do that because we want to add to the traditional view that temporary layoff is a component of total unemployment that plays a stabilizing role because of the high recall rates. We want to add a new, I mean, we're not the first to think about this phenomenon. Cats and Mayer were sort of in the 1990s discussing about the possibility of losing connection over time once you are in temporary layoff with previous employers. But we are the first to quantify this effect, to quantify this flow, to emphasize that workers who lose their recall option, they do so in a cyclical manner. They do so at higher rates during recessions. And we're also going to measure a new stock, which is going to be the stock of workers in jobless unemployment from temporary layoff, meaning workers who last exited employment through, you know, via a temporary layoff and then over time lost their recall option. And let me note that one team is going to be that this flows, recall and loss of recall are endogenous, and that's policy dependent. We're then one to understand, of course, what happened in the COVID-19 pandemic, where 50% of employed workers were moved to temporary layoff. And which is a distinguishing feature of this recession. A second distinguishing feature is that there was a very important response to this labor market dynamics, a fiscal package, the Paycheck Protection Program, which was larger than the Recovery Act, the 2009 Recovery Act. And was introduced to deliver forgivable loans to firms to preserve jobs, to preserve job retention. So we're going to study what role did PPP play in shaping employment recovery. And we find that this program indeed was successful. We find large effects. And in particular, we are going to show that these effects have acted through preventing loss of recall. This was Peter, we put this here. So I'm going to mainly focus on the empirics of temporary layoff and employment very briefly, give you the flavor of the model. The model is very rich. If I go through the details of the model, I would spend quite some time. So then I'll briefly tell you about the calibration of the model and move to the application. So in this part, we are going to argue that indeed temporary layoff is important for shaping the dynamics of unemployment over the cycle. So here we are going to start by looking at the stocks. And then I will look at the flow in the next slide. And you see a table with moments, first and second moments, regarding total unemployment, you, jobless unemployment, and temporary layoff unemployment. Now, both jobless and temporary layoff unemployment are strongly counter-cyclical, and they are also highly volatile. At the same time, in red, you see the temporary layoff and unemployment is only 1-8 of total unemployment. So you might conclude from this that temporary layoff and employment is not going to play a big role for unemployment dynamics. But the fact that the stock is low doesn't mean, of course, that the flow might instead be large. So here we are looking at the flows between the four states. And we also include in activity in pre-pandemic data. And as you can see, so first of all, in blue, you see that temporary layoff actually are account for 1-3rd of layoffs. So it has to be important to think about temporary layoff in this dimension. At the same time, if you focus on the second line, you're going to see that temporary layoff is a state which is very transient. The reason why it's very transient are two reasons for that. The first is that they have high exit rate. So they have high re-employment probabilities, as you can see in red. But they also have a high chance to exit that state to permanent jobless unemployment. So in green, you see the probability of losing the recall option of moving from TL to JL. Now, at the same time, this is actually an important table because it first of all emphasized that the re-employment probability of temporary layoff and employment is almost double than the re-employment probability of those workers in jobless unemployment. In addition, what we do here, we also look at the transition rate for workers in jobless unemployment, conditional on being in temporary layoff and employment in the previous period. So workers just lost their recall option. Why we do that? Because we want to provide further support to the idea that loss of recall is actually a meaningful phenomenon. This is measured based on survey. And so if it is, then we should expect that the probabilities of conditional workers in JL, conditional being TL yesterday, are actually very close to the unconditional probability of workers in JL. And that's what we find. They're almost undistinguishable and different from the probabilities of workers in temporary layoff and employment. So this also brings additional support to the idea that these two, as measured in CPS, are distinct labor market states, temporary layoff and employment, and jobless unemployment. Now, the last piece of evidence I want to show you is about cyclicality. So here, you see the cyclical properties of the five flows that we consider. And the main message is going to be that temporary layoff, E2TS, are particularly important during recession. So first of all, more employed workers are put on temporary layoff, so counter cyclical temporary layoff. In recession, fewer workers from temporary layoff are recalled to employment, so pro cyclical recall probabilities. More workers move from TL to JL, so counter cyclical loss of recall. This is going to suggest what we are going to call a direct and undine direct effect of temporary layoff. And in particular, we are going to argue that there are two ways in which temporary layoffs contributes to the increase in unemployment over our recession. What is the direct effect? And it's simply measured by the stock of workers in temporary layoff and employment. And this is going to increase because of higher layoff probability, temporary layoff probability, and lower recall probability. But there is also an indirect effect which cannot be measured by simply looking at the stocks. We need also to look at the flows because the stocks are not going to measure those workers who initially exit to temporary layoff and employment and then lose their recall option over time. So the stock of workers in jobless unemployment from temporary layoff and employment, which is an indirect effect, which in turn is going to be associated to destabilizing effect. So what we are going to do, we are going to develop a methods to estimate this indirect effect. So we are going to estimate a time series for workers in jobless unemployment who came from temporary layoff and employment. And we are going to look at the property of that time series. So let me now show you some data. So this is a plot of temporary layoff and employment in pre-pandemic data. As you can see, temporary layoff and employment is, as we already showed with numbers, is extremely counter-cyclical. But you also see some diminishing cyclicality, especially after the 1980s recession. This is actually one of the reasons that while the past literature has been focusing quite a lot on temporary layoff and employment in recent years until COVID, of course, there's been less attention to this labor market state. Now, however, if you add to this stock the new stock that we compute, temporary layoff and employment from, sorry, jobless unemployment coming from temporary layoff and employment, then you see a different picture. In particular, now we are measuring both the direct and the indirect contribution of temporary layoff and employment to the increase of unemployment over recessions. And we see that, especially in the later years, this is important during the Great Recession, the contribution of temporary layoff and employment to overall unemployment almost double because of the indirect effect. Now, of course, if you then put COVID, you don't see anything else, but what's important to see here is that most, during COVID, the things are different because most of the increase in total unemployment is accounted by the direct effect. And we're going to argue that the fact that the indirect effect was so small was largely due to policy, and in particular to the PPP fiscal plan. Okay, so let me now show you the model. So this is a rich model because we have three states and we model endogenous flows from the three states. So we are going to have, so the starting point, first of all, is an RBC model with search and match infriction, a general equilibrium model with perfect consumption insurance and wage agility via staggered nush bargaining as in previous work I had with Mark Gertler. And key variation to that setup are going to be endogenous separation into temporary layoff and employment, as well as endogenous separation into jobless unemployment. So we move away from this typical exogenous separation assumption and we have both separation into temporary and permanent unemployment. We're going to have recall hiring from temporary layoff and employment and standard new hiring from jobless unemployment. And we, this is a twist, so we don't have time to talk about that, but we also allow for temporary pay cuts to limit the extent of inefficient separation. Due to wage agility. Okay, so some details and then I'll stop with the model. So we're going to have workers who are unemployed, if they are in the jobless unemployment state, they search for work in a very standard DMP style matching market. If they are in temporary layoff and employment, they wait for recall or loss of recall. To model separation, temporary employment separation, we're going to assume overhead costs and in particular idiosyncratic firm specific overhead costs which is going to lead to the firm to shut down. So to separation into jobless unemployment of existing workers. At the same time, the firm will have some stock of temporary layoff workers who are on temporary layoff and those workers will also endogenously lose their recall option because the firm shut down. And then we have worker specific overhead costs which are going to lead to separation into temporary layoff. So firms are going to put some workers above a certain threshold for the overhead costs, they're going to put a fraction of their workers on temporary layoff. For surviving firm then after separation firms are going to run their business. If they survive, they're going to run capital, hire from jobless unemployment, recall from temporary layoff. And the way we model iron and recall is through some adjustment costs, cost of adjusting the labor force. They are going to be symmetric but with different parameters which we are going to estimate in the data to match the different elasticity of recalls and new hires to the firm job value. And I already said about Nash bargaining. Okay, so we calibrate the model based on pre-pandemic data. What we do, we match standard labor market stock and flows, but we also of course match moments that regards temporary layoff, the stocks of temporary layoff and the flows in and out of temporary layoff. We use both long run moments and busy cycle feature. And so this is just one small thing I want to say. So we target some volatilities, some labor market volatilities. We don't ask the model to match those. We target some volatility of TL unemployment, JL unemployment, but we tie our ends in a number of ways and the model does pretty well in matching those data. So let me now conclude with the application to the COVID-19 recession. So first of all, let me clarify that we do not have an epidemiological model. So we do not model the endogenous spread of the virus. What we do, we capture the economic effect of COVID through introducing two structure shocks. One, and this is new in the literature are, because there are other papers who have been modeling COVID through lockdown shocks. So these are MIT shocks and they're going to move workers from employment to temporary layoff and employment. There is going to be a distinction between workers who are in temporary layoff through lockdowns and workers who are in temporary layoff through endogenous temporary layoff. But they go to lockdown. And then we modeled the consequence of social distancing through shocks to effective TFP, which we interpret as a reduction in the utilization of capital and labor. And we are going to add two parameters that are specific to the workers in lockdown. So we're going to allow and we're going to estimate these parameters together with the shocks. We are going to allow for the possibility that workers who are in temporary layoff due to lockdown might have a different probability of moving to jobless unemployment, of losing their recall option. And indeed, we're going to estimate that the degree of attachment of those workers in temporary layoff due to lockdown as opposed to a standard temporarily layoff, a slightly higher degree of attachment. And we are also going to allow for the possibility that recalling back those workers during COVID might imply lower or different adjustment costs to firms. We're going also to introduce PPP. We are going to follow Kaplan-Mole Violante. PPP is going to be a diet model as a diet factor payment. We of course are going to calibrate the size of the program, 12.5% of GDP in the first two rounds and about 5% of GDP in the second round. We're going to assume that 85% of that amount was actually forgiven. And we're going to assume that the program is unexpected, that the funds are used when they are allocated and that after the announcement, the availability of the funds and how they evolve is known. Then we estimate the shocks, the series for the shocks, and we estimate the two additional parameters too much the evolution of the stocks and the evolution of the flows during the COVID. And we do well. So finally, I have a couple of slides and then I'm done. So we study the role of policy. So we keep decision rule parameters and shocks, but we remove PPP. And what we find is that actually PPP was successful in what it was intended to do. So prevent destruction of matches and encourage job retention. We find monthly employment gains of more than two percentage points in the first six months with those gains that are fading out over time, but they are still about 1% after more than a year. In particular, the mechanism is going to be the following. The PPP simulated recalls. The cumulative number of recalls over this first six months actually double because of PPP and because of higher recalls. So this also induces lower reduction in the loss of recall. And to make this point more powerful in a more visually. So what we do, we plot here three series. So that's a bit of an unusual way to show a counterfactual, but so we have in blue, temporary layoff and employment in the data. In red, we have temporary layoff and employment plus jobless unemployment from temporary layoff and employment in the data. So that's the diet and the indirect effect of temporary layoff and employment in the data during the COVID recession. And here what we are plotting, the difference between the blue and the red is the increase in jobless unemployment from temporary layoff and employment in the counterfactual, absent PPP. And as you can see, there is a significant effect in terms of preventing workers of moving from temporary layoff and employment to jobless unemployment. So let me conclude by just mentioning directions for future work. So in the paper that we wrote, the cost of loss of recall is that moving to jobless unemployment means inheriting lower re-employment rates. But there is a different type of cost and we don't have that in the model, which is the fact that loss of recall is also going to dissipate much specific capital. So it would be interesting to consider heterogeneous much quality. And once you start thinking about much specific capital, there is also this idea that has been put forward actually by Davies and Coters, by Steve Davies and Coters, that these programs might actually add a cost. They preserve matches, they preserve employment, but they might have hindered reallocation. And this could be particularly true for PPP because PPP was targeting smaller firms. And so this might have hindered efficient reallocation. So I think I'm in time. I wanted to show another picture, but maybe later if it comes out in the discussion, can I go back to the slides in case? Okay, good. So thanks. Thank you very much. Antonella, thanks a lot. I think I forgot to mention that you come from Bocconi University, so at least I should do it now. And with this, I would like to give the floor right away to Fabien Postelviné from the University College London and the Institute for Fiscal Studies with his discussion of the paper. Thank you very much. Thank you very much. So thank you, Antonella, for a crystal clear presentation and thanks to the organizers for giving me the opportunity to read this paper and document myself about temporary layoffs in sort of a more precise way than I probably would have done otherwise. So I'm glad I did. Let me start with maybe a short history of the thinking about temporary layoffs in, I suppose, macro labor. For a long time, as Antonella has kind of hinted, the conventional view has been the temporary layoffs are not important because they're a very small fraction of the total stock of unemployment. Now recently, a more recent literature starting with Hall and Kudliagda, Masturini and Fujita, et cetera, have emphasized that flows between temporary layoffs and employment are important and cyclical. Therefore, temporary layoffs are important to understand unemployment dynamics. Now what, oh, I should mention as well that obviously even the stock of workers on temporary layoffs, as we've seen in a picture that Antonella showed, has become, you know, first order importance in April 2020 with the pandemic recession. And that's obviously been emphasized in a very recent literature. Now this paper highlights the role of a new flow or another flow, which is large and cyclical, and that's the flow between temporary layoffs and what the authors call jobless unemployment, what others have called unemployment without recallers. I can't remember what the other terms were, but this is what standard labor economists would think of as being unemployment. So that's my brief history. I think this is an extremely fair point that the authors sort of motivate their paper on. Indeed, in the data these flows are large. The authors put special emphasis on the flow from so temporary layoffs to jobless unemployment, which they term loss of recall. If you look at numbers, this is the table that Antonella showed at the beginning of her presentation. Those are flow rates between a different labor, the three, sorry, the four labor market states that are emphasized in the paper. So here the flows from temporary layoff to employment, 43.5% from jobless unemployment to employment on average over the 35 year period that Antonella is looking at, 24.4%. So a little bit over half that of temporary layoff to unemployment. And so workers on temporary layoffs have a much higher probability of returning to employment than workers on who are just unemployed. What this paper emphasizes is this number highlighted in red here is that the flow from temporary layoff into jobless unemployment is quite substantial, it's almost 20%. Now, one number that is not so much emphasized in the paper and which I'd like to emphasize now is that 2.2% of jobless unemployment to temporary layoffs. If you just crank out the numbers very quickly, the jobless stock, the stock of jobless unemployed is about a little bit less than seven times larger than the stock of workers on temporary layoff. And so if you take 2.2% of stock that's seven times larger, you get in levels, you get a number of workers who move from jobless unemployment to temporary layoffs that is sort of not very far or at least a similar order of magnitude to the number who move from temporary layoff to jobless unemployment. And that sort of begs the question of what is it that we're measuring here? What does it mean to go from jobless unemployment to moving back to temporary layoffs? Temporary layoff is a situation where you expect to be recalled to your old employer. So what does it mean to go back after a certain duration in unemployment into temporary layoff? And more generally, the question I want to ask here is what is measured, what does this temporary layoff label that is seen in the, that is constructed by the authors from CPS data, what exactly does it measure? Now, what the authors say here, the way the authors interpret it, interpret a move from temporary layoff to jobless unemployment is as a loss of recall. And what they say, I'm gonna give a quote from the paper, if a transition from temporary layoff to jobless unemployment represents a true loss of recall, and I've just lost my timer, it's back. Then we would expect the re-employment probability of such workers to be similar to the unconditional re-employment probability of workers in jobless unemployment. Otherwise, we would expect the re-employment probability of workers moving from temporary layoff to jobless unemployment to remain high. But, I mean, that might well be true in a world with constant hazard or in a completely unconditional world, but if you introduce, for any reason, duration dependence, for example, in the process of returning to employment from unemployment, and if the temporary layoff label is correlated in some way with short durations, then we would expect the exact same patterns to occur as highlighted by the authors. So, I've sort of had this sort of half-jokey title here of my silly model of temporary layoff, so let me give you what I cannot characterize otherwise as a silly model of temporary layoffs. So, a labor market in steady state, so I have nothing to say about cyclicality here. Workers can be either employed or unemployed, just like the authors do in their theory part, I just condition out an activity here for simplicity. When employed workers in my simplified world face the same IID job loss risk, and when unemployed, well, each worker, which I'll label as I, has an individual specific job finding probability, fi, so some workers have a high, actually a job finding probability of one per period, so they find a job with certainty at the end of one month, and a fraction alpha of workers have a lower job finding probability, some number between zero and one, okay? So, not a very sophisticated model. And then on top of that, I'm going to affix a label to each worker, a type TI, either TL or JL. That type is going to change stochastically over time following some stochastic process, which essentially is a first order, it's not exactly a first order Markov in my simulation, but essentially that, it's completely independent of their job finding rate. So, in that sense, my label TL or JL in this model is a completely meaningless label, it's just a label. And then I try to fit my model to the author's data, and the, well, the transition matrices that the authors show in the paper, and well, here's the result. I mean, it's not perfect, but it's not bad for such a silly model, and in particular, it's able to capture the fact that the probability of moving from temporary layoff to employment is apparently much higher than that of moving from jobless unemployment to employment. And also the fact that if you condition on being previously in temporary layoff, people who are in jobless unemployment have a much lower probability of returning to a job. So the key features that, you know, the key numbers that the authors emphasize are actually replicated by this model where temporary layoff or jobless unemployment is a meaningless label. Now, the mechanism, of course, in this model is that it takes at least one month for workers to make a temporary layoff to jobless unemployment transition because that label gets changed every month, or it gets changed with some probability every month. So thus, all of the high job finding rate workers are gone by the time that the first job, you know, the first label change occurs. And so workers in the JL previously in TL samples, so who used to be in the temporary layoff state and are now in jobless unemployment, are selected, they're negatively selected in the sense that they're all the people with low job finding rates. Now, of course, again, let me emphasize, I don't believe that this is the true model of what's going on. This is a silly model, I can't emphasize that enough. But it does replicate this aspect of the data, at least. And I was wondering whether those, anything that the authors could say of how much of that is going on in the data. And I think the empirical part of the paper would benefit from having a little bit more sort of in-depth conditioning on observables and maybe duration analysis in the sort of diagnosis, I guess, about measurement, what it is that is measured by temporary layoffs. So that was my, I guess, my points about the empirical part of the paper. Let me just very briefly move to the model, the G.H.T. model, as in Gertler, Hockfeld, Tragari. So yeah, this slide is a festival of acronyms, I'm sorry about that, but so the G.H.T. model builds upon the Gertler and Tragari model, which is a classic paper published in, well, 13 years ago in the JPE. It's a very sophisticated, contrary to my silly model, it's a very sophisticated DSG model with matching Frictions, Ala, Diamond Mornston, Peseritas, featuring a whole large number of moving parts. In particular, worker-level transitory idiosyncratic cost shocks causing workers, well, causing temporary layoffs, job or firm level idiosyncratic cost shocks causing permanent firm closures and job destruction, and a whole array of other things such as real wage inertia, capital, capacity utilization, et cetera, et cetera. So very sophisticated machinery. On the other hand, the model only has one aggregate shock to TFP, at least outside of COVID times, and it does a very good job of mimicking 35 years of data, roughly 35 years of aggregate data on labor market dynamics, so that's quite impressive. Now, I wanna make two points about the model, at least one and a half point, I guess. So the authors describe, as Antonella emphasized during her conclusion, temporary layoffs was destabilizing. So they say, we place particular emphasis on the following destabilizing effect of temporary layoffs, namely that a sizable fraction of workers who initially exit employment for temporary layoffs are not recalled. Well, that's true, but in the model, is it a good thing or a bad thing that, I mean, temporary layoffs in the model, they're a good thing. The possibility of recall is a way for firms to escape search costs. You wouldn't wanna get rid of temporary layoffs in that model. In fact, that same model could be interpreted as a, it's just as an aside, just because I come from the UK, but this same model could probably be used as a representation of UK style zero hour contracts, some kind of flexible type of work scheduling, and it would say that this is also a good thing. Something that's up for debate, but in this model where essentially workers are risk neutral because there's insurance within the family, there's search costs that can be circumvented using temporary layoffs, they're a good thing, temporary layoffs. So the question I guess now is, what policy conclusions can be drawn? I mean, we're in a very sophisticated model with rigidity and with matching frictions with externalities, et cetera, et cetera. So is it the case that in this model, private job destruction, job creation decisions and separations into temporary layoffs, are they suboptimal in some way? Do they differ from the planners? Is there some form of dynamic inefficiency? I guess those are questions that I think would be very interesting to address with this model. And finally, just in my last minute and a half, talking about the pandemic and the PPP policy. So, well, you have to sort of give it a little bit of help with extra shocks, but the model does do an impressive job of capturing aggregate labor market dynamics during the pandemic, even though it was calibrated to pre-pandemic data. And one of the author's messages in the paper is that PPP was successful in fulfilling its intended purpose of encouraging firms to rehire workers on temporary layoffs. So this is just a small point I want to make here and it's kind of unfair to make that point now in a way because it's a point that could apply to a lot of the literature around PPP. But the way that PPP is modeled in the paper looks very much like a free lunch given to the economy. It's essentially, it's a bit more sophisticated than that, but it's essentially a positive productivity shock that partially offsets the negative COVID shock. So as such, it's not entirely surprising that it encourages firms or it sort of offsets some of the negative effects of the COVID shock. And so I guess my final point here is that probably, it'd be useful to sort of go a little bit further and build in sort of anticipations of having to pay for PPP a little bit later maybe and see how that changes the results. And with my seven seconds, five seconds to go, I'm just gonna close here and thank you very much. Thanks. Thank you very much also for sticking for the time limit. So perfectly, maybe I just give the floor quickly first to you, maybe you want to react directly. Yeah, sure, sure, so yeah. Thanks Fabienne for this insightful discussion. Of course, all the points you raised are well taken. So let me start with very briefly to comment on what is this measure of temporary layoff versus jobless unemployment. And I would like to emphasize here that we, in using this measure, we are within a very large literature that goes back to cut some million in the 90s that has been revamped recently because of COVID. And of course, there is also alternative literature that think about recalls and this is associated to the seminal paper in particular by Fujita Moskari and think about recalls and unemployment in a slightly different way without really distinguishing between these two states using the CPS data as we do. And this important paper has actually encouraged all others that have been working with this definition to sort of justify better. And we sort of entertained this a bit later. So what we did was to show this table. We produced this table where we have this right constant as a rate. Let me actually say that with this transition metrics we can actually replicate some duration dependence data that have been documented by other people. But there is also other type of evidence which points to the fact that workers in temporary layoff and employment do behave differently. They do have behavior which is different. They do search much less than workers in... And so even if it was for that fact that would be a meaningful distinction that workers base their behavior on their actual perception of whether they're going to be recalled back. But yes, it's a well-taken point we need to deepen a bit more this aspect as has been done by other people. Now regarding this 2.2% flow of people move from JL to TL actually that's, we don't have that in the model because it's much smaller at least in terms of probabilities but you can think of part of it being measurement error of course and part of it being actually workers who realize that they didn't think they had in their hand an option to be recalled and actually they do at a later stage. So I don't think that's a completely absurd phenomenon. Now regarding the policy prescription we didn't get into this, we didn't entertain this and we didn't do it for a number of reasons including the fact that we would be missing very important aspect that I mentioned at the end of my presentation of recalls, we need to have heterogeneous much quality and think about reallocation and preserving much capital. Now, yes, and finally yes to your last small comment, yes, it's through that, it's maybe not so surprising that there's PPP achieve what we did. But okay, so first of all the point of the paper is to quantify it, it's a quantitative paper so what we want to do is to quantify it and we also have a structural model and so it's not only quantified but emphasize the mechanism and in particular emphasize the importance of this additional flow of mitigating this additional flow that we document but yes, we could somehow take into account the fact that this is not a free lunch and there will be some, thank you. Thank you, so floor is open to any questions from the audience, if there are none then I of course have a question but if there is then please go ahead because I mean for my question that I had is because this is again paper based on US data and now when you look at also a bit the success story that we had in Europe during COVID with this short term or this short, how do you call it, this short basically short-time work schemes they were kind of a bit different in the setup so it was not so much loans to the companies but rather direct transfer, would you think that this kind of mechanisms, I mean that it matters whether you do it like as a loan or whether you do it as a direct transfer or would that not change the results? Okay, so actually the picture I wanted to show was exactly about the difference because I was expecting people thinking about Euro versus the US and so the one thing, it's at the very end of the, it's the last picture I added it today, it's not part of the paper but I thought it was interesting to make this point here. Okay, so here you see in the first plot you see the unemployment rate, it's a different scale just to make the dynamics comparable in the United States and in the Euro area and when you look at the COVID recession you see this enormous jump in unemployment in the US and in relative terms a very small increase in the unemployment rate in the Euro area and that has been, sometimes ago I was super surprised about that and then I discovered that actually temporary layoff, they do exist and they have a different definition but they have been used. It's part of these job retention schemes that you mentioned is just one of those but they've been used, they're just counted differently. Temporary layoff workers in the US are counted among the unemployed and temporary layoff workers are counted among the employed in the Euro area so then I constructed this to counterfactual where in one case I take away temporary layoff workers from US unemployment in the other add temporary layoff workers in the Euro area to the unemployed and then you see one is the middle plot and the other is the, and you see that the dynamics are strikingly similar when you do that. Now of course, the most substantial question is whether these objects are the same and whether they work differently or not. And even the definition are different because in the United States the definition, so you workers are classified as being on temporary layoff if they have an indication of a date, some time in the future to be going back to their previous employer or if they have some expectations that in the next six months they will go back. So this is the definition. Instead in the Euro area the definition is a bit more, is stricter because you are on temporary layoff if you have an expectation to go back to work within the next three months or if and not or and you are receiving at least 50% of your wage. So that's a much definition with a much stronger attachment than in the US. So presumably it would be interesting to try to measure loss of recall which must be present also in Europe and the extent to it but I expect it to be different. Now this is a bit orthogonal to what you ask which is more about I guess the, so I haven't thought about loan versus subsidies. But it goes into directly this direction that if you have a different mechanism you still show that it doesn't really matter too much for the results of your paper apparently. So that I think is an interesting result. Now with this I would like to give the floor and for the audience the chance to also ask questions. Check whether there's anything going on online but apparently not. Luke has a question. I think we can. So Antonella, I understand you have to rush a bit over your model with time constraints but the unimportant parameter is this firm specific overhead costs that you introduce. And I was just wondering for the benefit of the audience if you could give an example of what they would capture in the real world. Yeah, so it would capture costs at the, that are not directly associated to the use of a particular input. So you can think of costs of running the business like paying the rents, paying the utilities, administrative costs, that's the idea. And actually the PPP was designed to be used, firms could use PPP both to pay for wages and to pay for this kind of cost which they, at least part of those they were still in place even though they were not operating during COVID. Yes, Francesco Lippi, he's the mind. Thank you. So just a clarification about a point that was raised by Fabien. Who are these agents moving from the jobless unemployment to the recall unemployment? Is this like some statistical? Yes. Or is there really like a type that can, because if I lose a job, I lose a job then I'm not attached to anything. So what does it mean to go back to a temporary unemployment? Yeah, so we haven't digged into that flow because it's smaller at least in terms of, the probability of making that transition. And we don't have that flow in the model so maybe we could think a bit more about that. The way we thought about it was either measurement error which is, this is a CPS, it's a survey, we do have measurement error. Or it could also capture an actual phenomenon of workers that initially they just think they lost their job permanently. And then something comes out, some new information, and they realize actually they have a recall option standing in place or maybe that there is some effort in the very short term of the firm to reconnect. I mean, there, you know, yeah, this kind of. So thank you very much to both of you, both Antonella and Fabien. And with this we conclude our first session of this conference and we now here in the hall have the chance to go for coffee. The break is 15 minutes and then I hope to see you all back either online or here in the room and looking forward to the next session. So thanks again, thank you very much.