 So, this is the session 2 of the conference inflation effects of supply disruption. So we have 2 very interesting paper. So, 1st, from the benefits of international business economics, it's going to present the paper, the gospel effect of global supply chain disruption on macroeconomic outcomes theory and ideas. And then, like Frank Smith, it's going to discuss that you have. 25 minutes. Okay, I'm just going to make a start. Thank you so much for having me and also having our paper in the program. So this is a joint work with issues she won Francesco and so it's very preliminary. So any comments and questions are welcome. So, I have to say we had Dennis and I, we had a short discussion before this meeting. And so we were kind of wondering whether the ordering of our presentations could be altered because as you will see, Dennis's paper is more on the application of the global supply chain pressure index. Compiled by the New York's fed, but our papers more on hitting that very hard because we're trying to create a new index and show it. It is a better index. So feel free to leave your comments in the end without further ado. Let me stop the presentation. So we have a very big and Bella question in the front, which is what are the causal effects and policy implications of global supply chain disruptions on the US economy. So this is a very broad question. So we break that down into actually 3 parts. So the first thing is we wanted to know what are the shortcomings of those existing measures of global supply chain disruptions. And if you deep deep into the literature is this line of literature, you will see that a majority of them centered on essentially 2 pillars. So the first pillar are the transportation costs here. I mean, shipping prices of, you know, flight, you know, fly essentially shipping prices. And then the other one is the purchasing managers index, which includes something like delivery times backlogs. So, as I will argue later, so this 2 measures have their own shortcomings in particular for transportation costs because we know that it is a price measure. So inherently it will internalize any changes in prices coming from the demand side. So it's not pure enough. On the other hand, in terms of the PMI index, because it is based on responses to survey questions from those purchasing managers. Yeah, but that will subject to very large measurement errors. So amount, you know, given this 2 indices. So there is a very famous one by the New York feds global supply chain pressure index, which has been used. Not only in academic, but also in policies. You know, it's also building on this 2 components transportation costs and PMI. And I will show you later. Comparing the results, you know, our results was theirs. You will see very different readings of the causal effects of global supply chain disruptions, at least for the US economy. So this is the 1st question and then moving on. We wanted to know how does this supply chain disruption shock differ from other shocks because. You know, this question is directly speaks to this very vast literature on this entanglement of shocks during the pandemic period. As we know, a convolution of demand shocks, labor supply shocks and supply chain disturbances hit the global economy very hardly. So I will define what do we mean by supply chain disruption later on actually in the next slide, but this is the 2nd question we wanted to answer. And then finally, you know, we wanted to know what are the policy implications. So 1st of all, and actually you will wonder, you know, whether the policy institutions should opt for on, you know, multi tightening on a whole study approach. You know, in, you know, in response to the heightened inflation, for example, in the United States. So I got this idea from the, from the article by Philip, Philip Lane. But then I, I, you know, we actually, we go a little bit further by asking, you know, does this supply chain disruption alter the stabilization bias. Of monetary policy in controlling inflation and output, because you will, you will, you know, I think this is kind of related to these 3 presentations in the morning, because as long as we know that the stabilization by stabilization tradeoff has sort of changed. Then maybe there's a possibility of a soft landing for the US economy because type monetary tightening could do more than, you know, what it was intended in the very beginning. But this is this, you know, the last question we wanted to answer in this paper. So let me go through some of the things that we have done in this paper. So first of all, I wanted to tell you that we actually measure supply chain disruptions using this notion of port congestion. So as you may know that, you know, this container ports, you know, major container ports, they were actually responsible for more than 60% of the total value of seaborne trade worldwide. So they are very influential. But at the same time, I'm sure, you know, all of you have read those, for example, newspapers from the Financial Times. Also those reports from the IMF in the White House on, you know, the importance of studying port congestion in terms of global supply chain disruption. So that's why we take this very stance. But this is not an easy task because without the proper data and proper to you will not be able to quantify port congestion. So that's why we develop a new spatial clustering algorithm based on machine learning to essentially transform this very high frequencies that, you know, set line data of container ships into a high frequency measure of port congestion actually applicable to major ports worldwide. So this is very influential. But, you know, we just basically going to use this measure as a global supply chain disruption measure throughout the paper. So this is the first thing that we we're doing this paper and then since eventually we wanted to use our measurement and our theory to recover the causal effects of global supply chain disruptions. And in the realm of structural ours, we need some identification restrictions. So that's why we opt for a novel analytic theory to study the role of spare capacity resulting from the mismatch between supply and demand. The idea is very simple. So as you may know, you know, this during the pandemic global supply chain disruptions actually to very high level of transportation costs in particular shipping price. The key mechanism in our model is that, okay, now the shipping price is at very high level that leads to, you know, reduce the probability of a profitable trade. So thinking of myself as an exporter in Shanghai and my course of Francesco as an importer in Los Angeles. So if I'm going to pay a very high price to ship my good to Francesco in Los Angeles, and this price is so high that it's no longer profitable for me to make this trade, then trade will collapse. And then that will have macro consequences in terms of spare capacity and all the other stuff. So this is the key mechanism we have in the theory, but in the end we managed to come up with some very unique identification restrictions for the demand shock for the labor supply shock and also supply chain disturbances, which we could use alongside our measurement into structural barriers for the causality assessment. And then, as I mentioned, the third thing would of course be the causality assessment to integrate both of them into the into the discussion. And in this part, we actually compare our measure, the results using our measure with those, you know, using the JCPI and you will see that they are quite different in terms of the results. And then lastly, I think it is very closely related to three presentations in the morning, which is a state dependence analysis, studying the interplay between the supply chain disruptions and the factors effectiveness of monetary policy in controlling inflation and output. And so just in a nutshell, we actually predict the possibility of a soft landing for the US economy. So this is somehow consistent with, for example, the presentation given by God in the morning. So in the interest of time, I'm not going to the details of this, you know, this lines of literature that we connect to. But essentially, we have, you know, theory of this equilibrium, for example, building on some work by Michelin and size. And also, Michel Gaspi, who is also here in the present in this conference, and also we connect to transportation sector and also the entanglement of supply chain disturbances. So, let me jump straight away to the measurement of global supply chain disruption, because according to the micro also is the longest name. This is the most interesting part of the paper. So, let me try to go through this with you very, very slowly, just to just to highlight, you know, this measure is really something influential. So, like I mentioned, we measure disruptions to the supply chain by studying congestion at those container ports. And then how we do this. So we go back to the, maybe this is somehow I have to push you away from the comfort zone by going into the maritime literature, because in maritime economics, they have a very clear measure of poor congestion, which is the likelihood that a container ship will first more in an anchorage within the port before docking at burst. So, even though those two terms might sound quite technical, but you can think of the anchorage as a random area in the port where a container ship can lower its anchor. So this is quite random. But then for a burst is actually a designated sport in the port where the container ship will load and unload his cargo. So the key question is whether, you know, if there were no congestion, no port congestion at all, you know, naturally, if a container ship, for example, you know, going into the ports of Los Angeles and Long Beach, it will directly go to the burst to have their, to have his cargo loaded or unloaded. But it's just because port, because port congestion, it is not able to do so anymore because there are few ships, a lot of ships waiting ahead of this container ship. So, you know, this ship, very ship has to wait in the anchorage. So we, in order to quantify port congestion using this definition, we essentially need two pieces of information and tools. So the first one is we will need information on the movements of those container ships. So we do this through the help of the automatic identification system. So this is a very, it was actually there for quite a long time, but the data was only available after 2017. But essentially, this is a system installed all the vessels to track its movements, you know, essentially 24 seven and across the entire world. And then that's the information, you know, the data source. But then in order to process this data, because this, this is data actually update, you know, is actually updated every two seconds, we need some heavy machinery in order to transform this high frequency data in something useful. So that's why we develop a machine learning spatial clustering algorithm. So that's why you see that I'm pushing you away from economics to process data and then find something useful. So, in order to illustrate all of this, let me just show you several figures because I think this is more intuitive. So as you can see in this figure, this is essentially a snapshot of the AIS observations outside the ports of Los Angeles and Long Beach since January 1st, 2020. So each of this blue dots represents a container ship essentially, you know, in that period. So what we wanted to do is actually to to distinguish whether some of the ships were actually waiting in the anchorage and some of the ships were, you know, loading all of this stuff in the universes. So to do this, we developed this algorithm. It's quite complicated, but it's essentially working in two layers, you know, based on, you know, density of more importance. And also some of the other information as granular as what I show in this figure, which is the headings of this container ships. For example, as you can see on the left hand panel here, the headings of the container ships are quite opposite when they docking at the burst. But if you look at them, when they more in an anchorage, you can, you really don't see a very clear pattern. So we use this sort of information in the machine learning algorithm and alongside the AIS data to come up with this identification results as you see in this very figure. So you can see that originally there were quite a lot of most, you know, all of them are blue dots essentially, but now we are using CN and purple markers to represents those container ships, you know, waiting in the anchorage area. But while for all the other colors, you know, representing those container ships docking at the burst. So we managed to do this identification results identification for all the major ports container. Sorry, for all the major container ports around the world. And let me try to. Sorry. So there was a, there's a hyperlink here, but we managed to do this for all the more major containers. So for Singapore, for road and also in the ocean, which is the second largest port in the world, we have all of them there. So if you're free to read, visit the paper later on. So now we essentially have the geographical boundaries of those different areas for all the major ports around the world. And then we have our essentially panel of the movements of containers, the data. So now it's time to know, it's time to just, you know, go back to them and trying to figure out, you know, what is the ratio of a container ship that more in an anchorage area before talking at a burst. So this is essentially how we define our congestion measure and later on to be used as a supply and disruption measure. So, if you come, if you, if you look at the panel here, you can see that before, I think before 20, sorry, before 2020 essentially this theories was fluctuating around this medium. I think it's around, you know, 18 or 1%. But then just before the pandemic actually dived down to to its historical minimum before shooting up significantly to around 27% and remain edited thereafter. So, let me jump a slide to what our measure, how our med compares to the GCPI. So again, we are using the red lines to represent our measure while using the black dog line represents the GCPI index by the New York Fed. You can clearly see there are 2 key differences. The 1st absolute comes from this part where the entire world was experiencing the 1st wave of the code. So in the GCPI, it had a very large jump and then it didn't stay there long before dying back down. So, you know, if we refer back to the paper by Julian, they were saying that, you know, this job was actually due to the initial Chinese lockdown. But then this client was actually due to the partial reopening of China and Europe at that time. But if you compare their measure to ours, you can see that at least the poor congestion was relatively, it was still there at the historical low minimum. So it doesn't, it doesn't really, we didn't, we didn't really have a supply chain disruption at that time. And then the other difference comes from the later part of the sample where the GCPI has predicted a decline, but our measure was still high there. So going back to the previous slide, what we are trying to argue in this paper, as I briefly alluded in the very beginning was that the GCPI was not necessarily an exogenous measure of global supply chain disruptions. And the reasons were clear because the endogeneity issue of transportation cost and measurement arrows at the PMI. But for our measure, this ACR index, average congestion rate, we argue that it is very exogenous because due to the industry practice, it's very hard for those container ships to change their itineraries or routes. So that is to say, even you have demand changes around the world. For example, suddenly the states have very high demand for micro conductors, or in China there were suddenly very high demands for, I don't know, something else. This sort of changes in demand will not alter the trajectory of those container ships travel around the world. So anything that you see from poor congestion that rises, the falls would come from supply side alone. And also, since our measure is a global measure, essentially, no matter there's a small change in the congestion rate for somewhere in the world, it will be averaged out once we calculate this ACR index. And lastly, we argue that since it is using satellite data, it is accurate. So moving on to the second part of the paper, which is using the theory to get a set of identification restrictions, instead of going through the algebra and mathematics, I just wanted to highlight two key features in the model. Let's start from the second one, which is the indulgence separation of exporter-importer matches on transportation cost. So that is essentially the very, very example that I gave you a little bit earlier on my relationship with Francesco on exporting goods from Shanghai to Los Angeles. So that's the key. But in addition to that, we also have search frictions on the international product market that technically is to allow us to have a different curvature of the supply curve, which is going to be useful for the effectiveness of monetary policy. Nevertheless, let me just show you the key figure in our paper on the theoretical front. So what we managed to get from the model is a aggregate supply curve as the following. It has a steeper slope when we reach the essentially the bound of the production. And then using this framework, we managed to get several macro aggregates, notions of macro aggregates in this picture. First of all, in addition to the aggregate supply, we will have matching cost. We also have transportation cost. And adding them together, we will have a notion of spare capacity. And in the model, it happens to be that the spare capacity is essentially equal to our employment in the model's language. So that's what we have in the supply side of the economy. And if we augment the supply side of the economy with a very simple demand curve, we will have very interesting equilibrium dynamics that we can analyze to get this identification restrictions. For example, you know, this one that I show on the slide, which is the equilibrium dynamics following a demand, sorry, a demand shock. As you can see, the demand will move to the left and you have corresponding changes in prices, and so on so forth. But more importantly, I wanted to go straight to this table where we summarize all these sign restrictions, which we will impose on RAFs later on in the third part. So in particular, I wanted to highlight two parts. The first one is on price. The other one is on spare capacity. So start from the aggregate demand shock. I see. So you can see that when there is a negative demand shock price will go down and buying from and will go up. So that's very intuitive. But then if you compare between these two supply shock, so I think for most of the literature that right now, you know, online or whatever, they are looking at labor supply shock directly. And most importantly, they will predict that, you know, there will be a once there's a labor supply shock, there will be decreasing output, but a increasing price. But in this paper, we actually say that it will have a decrease, sorry, a decline in fact on unemployment as well. So the intuition is that given, you know, if there's a labor supply shock, essentially the size of the pie becomes smaller. And then if you have to allocate this pie to, let's say, a set of households, then you will need to, you know, more workers need to work a little bit more. So that's why I implement will go down, but this is not going to be the case if we look at supply chain disruption shock. So, in addition to, you know, a reduction in output and a increase in price, we actually predict an increase in our employment. So I think in most of the literature now, people don't really distinguish between these two supply shocks and later on in the SVR, I will show you this is very important. So, given the interest of time, I'm just going to go straight away to the comparison between our results and the result using JCPI index. So which I believe it's more interesting. So on the left hand panel here, you can see that this is the results using our ACR index and the identification restrictions that I just so show you in the previous table. So clearly I wanted to draw your attention to this panel here, which is essentially the, you know, the impact on inflation following a supply chain disruption shock. So I think this is consistent with some of most of the literature right now on the causal effects of supply chain disruption because it clearly shows that it is inflationary in this sense. And also you can see this as well for input price. But now, if we just replace our index with the JCPI from the New York Fed, you will not get this result. This is very clear from this second panel on the right hand side of this, of this very slide. Actually, it is predicting, you know, it, the response of inflation was relatively muted in the very beginning, but then it dived back down below zero after certain periods. And then this will have implications on for monetary policy, because if we go to the historical decomposition of the United States inflation in the United States on the left hand side panel here, you can clearly see that our result is predicting it. You know, before inflation in the United States in 2020 wasn't, you know, was largely because of a, you know, a reduction in demand because, you know, that was the first wave of covid sweat swept across the United States. But then you will not see this if we use the JCPI as a alternative. And also more importantly, if we use the JCPI, we're actually predicting supply chain disruptions contributing negatively to inflation in the United States. So this is clearly contradicting what we have in the theory part. And then for the remaining, I think I have two minutes, two minutes. Okay, I didn't even reach this far for all the previous presentation that I gave. So let me just go through this with you. And so, like I said in the very beginning, what we are interested in is whether the supply chain disruptions have altered the stabilization trade off of monetary policy. So we do this into, you know, parallel ways. First of all, we use our model to grasp some theoretical predictions. So in the model, we actually have this notion of contractional monetary policy shop through a reduction in minus supply. And then we also have this notion of supply chain disruption, which is essentially the distribution of transportation costs shifting to the right. And you can imagine that when the distribution of transportation costs shifting to the right, there will be more because I didn't mention this in the model part. But if there is a given threshold, let's call it the reservation threshold or transportation cost. When you have the distribution to the right, then there's a higher probability of transportation costs. The stores of transportation cost going beyond that threshold, making the trade less profitable. So that's the mechanism we have in hand, but using our theory and also some of the partial and cross derivatives. We managed to show, you know, those two figures, I wanted to draw your attention to the left the panel here. So if there were no congestion, nor global supply chain disruption, the economy will move from A to B. So that's what will happen. I think pretty much before COVID. But now, if there were supply chain disruptions, the economy would from move from C to D. And if you compare the distance horizontally and vertically, you can clearly see that this tradeoff has changed. And that was mostly driven by the shape of the supply curve in our model. So nevertheless, the takeaway from the model part is that we predict that a contractory monetary policy shock could tame inflation at smaller costs of output and employment. And then what we wanted to do is to test whether this is the case using a stretch threshold of ER model and skipping all the details. Going to the very end, I'm going to show you this rfs. So you can clearly see that if we use black and black shaded area, black line is black shaded areas to represent the impulse responses functions for the periods of supply chain disruption. While those of the red ones representing those without supply chain disruptions, for example, looking at real GDP, you can see that the reduction in real GDP on impact was way much smaller during supply chain disruptions. And more importantly, if we look at GDP deflator or impulse price, the responses of those price measures, they were greater in this supply chain disruption in these times of supply chain disruptions. And also you can see something that has been predicted for employment as well. So let me conclude. So what we wanted to answer in this very paper are the causal effects and policy implications of supply chain disruptions for the US economy. So this might be a little bit different from what Dennis will show you a little bit later on. So in order to do this, we develop a new index, we construct a new theory, and essentially we combine these two parts into the state of the art of assessing causality in time series. So there are three main results. First of all, as you will know already, supply chain disruptions generate stack inflation accompanied by an increase in spirit capacity. And the other one is that we cannot really get this results using the famous, famous measure from the New York Fed. And then lastly is, you know, the essentially the monetary policy of monetary policy, monetary tightening becomes a very much more powerful than before. So that's it. Thank you so much. Thank you. So now, Frank, it's a great pleasure to be here. It's also great to be back in this building brings back your tower brings back nice memories. Thanks for the invitation to discuss this paper. It's an excellent paper and why is it an excellent paper because as actually quite a number of papers in this this conference. It brings together new data with some new theory with some new empirical results and some policy implications. And if I can get my slides on the back then I can continue. So the new measure, the new measure was was very much explained by Lee and there's not much I will comment upon there. I mean, it does show how sort of this new big data, including satellite data can also be used in economics to measure the concepts that we are interested in. Thank you. So that's that's that's a very nice part of the paper. Then there's a nice intuitive model to analyze the effects of the rise in transportation costs, which has these two new features. There's a matching between exports and importers to model the congestion part the spare capacity part. And then there is an introduction of transportation costs, which really are treated as exogenous and create this endogenous separation. And then the main purpose of this this model is to provide identification restrictions and again, Lee mentioned the particular restrictions. And with those restrictions and this new measure of supplies change disruptions. The office find the very plausible empirical finding that the congestion shock, if I may call it. So, is that flationary and it accounts for a significant part of the rise in inflation, particularly in 2021. So, which is really the period when you had this big rise in or period after you had this big rise in congestion. And then it has what I thought was quite an intriguing policy implication that monitor policy in the model and in the data is more effective when you do have those supply restrictions. So when the congestion is high. To my view is a little bit counterintuitive, particularly after having heard gouty talk about how you need tight labor markets or tight markets to get no steep Phillips curve so it is a somewhat different intuition. So what I want to do in in the rest of my discussion is really talk a little bit about, I mean, basically give a number of smaller comments. I will not repeat the definition of the average congestion rate. I mean, just to tell you that in this picture was already shown that this measure very much correlates with other measures of transportation costs. And so one suggestion will actually be if you want to do a horse race, you should do part of the horse race with these other measures of transportation costs, rather than I would say with this, the New York feds global supply chain pressure index, which of course is a broader index of supply pressures. So three comments on on the measure. So it's actually a very specific example of global supply chain. It's really this congestion so it correlates very high with transportation costs. And of course one one can can have other definitions of supply chain pressures. I mean one one example which would also be very specific was the supply of microchips. I mean we know that there was a problem. This was not only associated with congestion in world ports. But of course it also had big impacts in the global supply network. I mean the time series is quite short. And you start, I guess, you have to end the sample somewhere with a sample which ends in July 22. This is when this congestion index is still very high. And so from the picture you see you basically have almost a step function. It's it's low and then it increases a lot and stays them. Now since July 22 this has come back again. And so I think to to robustify your results and probably also get more significant results. I would would extend the sample and also use the information that comes from the big drop in congestion. The paper big point is made about this this measure being exogenous and I mean I take the point that it is sort of more exogenous than say a measure of transportation prices. But I think it would be good to to also show some evidence that for example, is this indicator also cyclical or not. I mean some of the lockdown measures do they correlate with this congestion in the ports because a lot of congestion had also to do with labor supply in the ports. And I mean your argument for why it is exogenous. I mean, it's not completely convincing because let's say if global trade is booming, then actually there will be more ships being planned. And so if there's more ships coming into ports, then for sure your measure, which is sort of the share of chips that are chips that are in anchoring places versus the total calls is likely to be higher. So there could be some some some cyclical component there. And to some extent you see that a little bit in the VR reasons. Okay, so this is just to cast an illustration of these these three points. I mean, these are sort of the supply chain pressure heat map that we use at the ECB the transportation costs are are in this lower bar. I mean, depending on on which one you use, you can actually have quite different numbers. So for example, the freight transportation cost index, which is the last the third last column, that's picked actually very early. And so the the GS CPI probably picks up some of those those things too. Okay, let me do then go. It's a very nice intuitive model. I think I will not repeat the type of sign restrictions that come out of it. I think it's quite intuitive that transportation cause they work like a negative supply shock. But they have this different from labor supply shocks. They have this different effect on unemployment or on on capacity. So the fact that transportation costs change doesn't change the capacity to produce at at home. Of course, this will depend to a large extent on how you measure capacity, capacity is taken from the importers side. Actually, it will also be affected by this model. Okay, the first one that I already made. I'm not sure why you have these zero restrictions. I don't think you probably need them. And sort of they are a bit annoying because the paper flows very nicely from the data to the theory to the restrictions and then you add these zero restrictions, which, which, which I don't think probably you don't, you don't need. I mean, the model of course cannot capture the inflation persistence. It's actually model of the price, price level. And so if you want to talk about inflation persistence, you probably need to have a real supply chain network. You will need to have some some some price stickiness at the different modes and so on so forth. One minute. And then the third and this is very cheap. I was, I mean, it is a very nice model and it's a quite intuitive model and but it's also not an easy model. I was thinking Gauties model of this morning, which is also not that simple, but probably it can give you similar sign restrictions if you if the difference is really between a general demand shock, a labor supply shock and sort of imported input shock, which transportation costs is one example of then probably you get the same, same sort of sign restrictions on the plausible empirical results. Maybe let me just say that the responses of the, the, the, the new index, the new index, the new index, the new index, the new index, the new index. Say that the responses of the, the, the new index, the average congestion rate, they're very similar to the responses of import prices. Actually, I think I, I show. So, so this is for each of the three shocks on the left hand side you have the response of import prices. On the right hand side, you have the response of the ACR. So I'm not sure there's actually that much different information in those two series, at least for this sample. And I was wondering whether why if ACR is quite exogenous, why can't you use it just as an instrument for the imported price shock because then generalizes the set of results that you can get. And let me then just end with the last slide with the intriguing monetary policy finding. Again, I think it's intriguing because it's the opposite of what one would intuitively think. Definitely having listened to, to go to this morning price effects are larger when the market is tight. In this case, price that was in Gauties in this case price effects are larger when there's quite a bit of slack. And, and I think I'm not sure I fully got the intuition. So that's probably more my, my mistake. So very nice. Excellent. Thanks. Thanks. Right. So maybe you have some question for the audience and then you can answer Frank and also question. Yeah. And, and please identify yourself and the microphone. I'm from DCB. So I also thought this was a great paper. I just have one question on the monetary policy implications. So when I look at the response function, I also noticed that in the two different scenarios. The response of the federal funds rate is very different. Now that can be because the endogenous variable behave differently, but the policy rule is the same, but it could also be that there is a different policy rule into the, in the two different scenarios. So that would, you know, probably then could also explain a part of the difference in the other variable and not be only related to this difference in supply curves. Thank you. Thank you. Katya Peneva from the Federal Reserve Board. So I had my first question was how is your measure and I understand how it is constructed differently. But in terms of how, how are the implications of your major different from the vessel schedule availability or the number of seaboard containers. There are measures people have been looking now, I mean they've existed for a while, but we really have been looking at them the last two, three years. So other than differently constructed, what, what, what other things does your major bias over this alternative container measures. And in terms of studying the implications for inflation and monetary policy. I wonder if it might not be better to, to, to focus your measure more narrowly for goods prices. Because if that's where you're hitting the really steep nonlinear part of the supply curve. That's when, you know, even a little bit of a reduction in demand from monetary policy tightening can get you a big decline in inflation rather than just overall inflation or containers, you know, not used I'm guessing for oil and unless you're including those and they're not also used to transport services. So you can just focus it more narrowly. That's that's it. Thanks. Maybe you can answer this question. Thank you. First of all, thank you so much for giving this discussion on this paper. I find all of the comments very, very, very informative and I wanted to just talk about some of them. I may not be able to note down all of them, but there are several. So the 1st thing is about the exogeneity of our measure and also that speaks to your question. Just a little bit earlier, we actually start from this industrial practice because by talking to 1 of my courses, you know, she just told me that the schedules of those container ships, they will not be affected by changes in demand. So as put by a, you know, sorry, Julia Brancasio from New York University in their very famous econometric paper. So she mentioned that those container ships, they were very much like buses on the sea. So they don't really change a lot in terms of their behaviors. So that's why by looking at their behaviors and also examining them, we actually have a supply measure instead of a convolution of supply and demand. So that's initially essentially, you know, how we get this exogeneity and why we wanted to emphasize that in the paper. But I understand that there were some changes in, you know, the shipping capacity across routes during the COVID period. And in particular, there's an example for some companies changing, you know, actually shifting some capacity from the Asia Africa route to Asia, United States, North America route. So there, there have been some changes. But in the paper, we did several robustness checks to roll out this, you know, this effects of this sort of things changing capacity. For example, on the ACR and also the causal effects of global supply chain disruptions without going to the detail we actually find all the results are still robust. So this is the first thing. The second thing is on the intuition on why we actually find a more effective monetary tightening during periods of supply chain disruption and why this is going against what he has mentioned in the beginning. I think it is focusing. So let me explain our intuition. So it is actually very simple because when you have a, we have a much steeper flow of the aggregate supply curve as we have in our model that essentially changes the sensitivity of prices to movements in demand. And then this changes in the sensitivity will then translate translate into changes in the effectiveness of monetary policy because in our model, it is very simple. It's coming directly from a reduction money supply. It's essentially playing the same role as a contraction demand shock from other sources. So I don't, and then on the second thing, I don't really have a very good answer on why our result is kind of different from Gossies. But I would love to highlight that, you know, we are actually looking at the exports and imports this trade framework, instead of the labor market. I think if you know, maybe focusing one or the other, you will get completely different results. But nevertheless, this is my comments, but then there's one. There's another comment on the RFs of FFR in the last part of our paper, whether that is that may be due to, for example, changes in the policy rules or something else. That was a very good question. We actually, you know, we were actually doing something to to as a robustness check. So in particular, we were actually using a method by Sophocas on implementing zero lower bound in the structural ARs, because we know that during the post the COVID period, there were actually a very long period of very close to zero interest rates in the United States. So we wanted to tear that out using this new method. We're still working on it. Hence the results are not so clear at the moment. And then lastly, very also a very good suggestion on using the, you know, the, instead of using the GDP deflator, we can use the prices on the goods. So we will have a look at that as a robustness check in the paper. Thank you. Thanks a lot, Julian. So now we should probably move to the next presentation. So then it's a bonus gonna present us global supply chain pressure inflation implication for monetary policy. You have 25 minutes. Right. Let me start by thanking the organizers for putting the paper on this very great program. So this is joint work with Guido Scari and Andras Madu colleagues from the Dutch central bank. So, you know, the usual disclaimer applies. I want to start with this figure, which shows the global supply chain pressure index from the New York Fed, which Lee has spent some time bashing and being very critical about. So just going to ask you to forget what he said for the next 20 minutes. So, and then in red, we have your area core inflation. So, yeah, what you can see is that in the past 20 years, so there has been these spikes in global supply chain pressures due to various reasons we had the GFC natural disasters in Japan. And of course, the, the, the run up during, during COVID in different ways. And then if you apply some eyeballing econometrics, you can also see that inflation and these global supply chain pressures seem to kind of co move. I'll be that, you know, inflation is comes with a lack of it. So we have these global supply chain shocks, these spikes. At the same time, global value chains are very relevant for a lot of advanced economies. I show you here, one of the many measures that you can think of, of global value chain participation in a bunch of advanced economies. And you see that they are quite large, quite substantial have been training upwards until the GFC and then started to level off, but not really have been going down. Right. So, at least from this perspective, there's no risk of a deglobalization. In fact, in my own country, we still seem to rely more and more on global global value chains. So given the importance of these spikes, these shocks to global supply chain pressures and also given the importance of global value chains in advanced economies. What we want to know in this, this paper is what we ask is how much do these global supply chain pressures contribute to inflation in the euro area. And we're going to do this empirically in two ways. We're going to start off with a very simple Phil's curve analysis. And then we also have a more structural approach where we use a B var. And then secondly, in the theoretical part of the paper, we want to know what these global supply chain pressure shocks, what they imply for optimal monetary policy. And to this end, we are going to use a new Keynesian model to countries that features in a very stylized way global supply chains. And what we find in a nutshell is that the global supply chain pressures contribute positively and significantly to inflation in the euro area. So this is what we find from the Phil's curve analysis. And the B var is going to tell us that shocks to these supply chain pressures were the dominant driver of inflation in 2022. And also have a highly persistent and home shaped effect on inflation. Now, the model, the new Keynesian model is going to tell us that the optimal response to this type of shock that raises inflation is going for monetary policy to tighten. However, the aggressiveness of this tightening is going to be a nonlinear function of how much a country relies on global value change. Okay, so in the interest of time, I'm going to skip this slide, but suffice to say that there are a lot of papers that are related to this topic. And it seems that every week I find another paper that is written on this. Okay, so starting with the empirical part of the paper. Like I said, we take a two prong approach. We start off with a Phil's curve analysis. And then we have a B var. And then the Phil's curve analysis is going to be very simple. So we have, you know, for the euro area, we're going to regress inflation on its lacks some measure of slack expectations. That's going to be our baseline Phil's curve. And then we're going to compare this with an augmented Phil's curve that we add the GSCP is an additional regressor and see what happens. Okay. So this is our, these are the results. So we're going to use monthly data. So use industrial production as our slack measure. And as you can see, in columns three and four, the coefficient on this GCP I is positive and significant. We're not going to claim any causality here. This is just, you know, for illustration, basically. But it does suggest that there is some positive co-movement between these global supply chain pressures and your area inflation. Now, another noticeable result was that, you know, if you look at the estimate for the slope of the Phil's curve, which is a top row. And you compare, you know, the baseline model with the augmented model. This slope is quite big, a bit larger than when you include the GCP I measure. So, you know, this gives you some indication that by adding some external factor in your Phil's curve, this helps you to better identify the Phil's curve slope. And, you know, helps you avoid finding a flat Phil's curve. Then over to the B var. So again, we have a monthly data. We're going to use six variables, five aggregate variables. So these are industrial production, core inflation, the 10 year OS rate to capture the monitor stands, real effective exchange rate and energy prices. And I'm going to add the GCP I. So we're going to impose a number of restrictions, sign restrictions, zero restrictions. We have demand shocks that, you know, that are very standard in the sense that they move output and prices and interest rates in the same direction. And then we have domestic and global supply shocks. And here the, the way we try to distinguish between the two is to impose that the domestic supply shock has no contemporaneous impact on the GCP I, whereas global supply shocks, you know, lead to an increase in the GCP I on impact. And then we have a bunch of other shocks that we want to identify in order to kind of build a narrative on the relative importance of these shocks. So in addition to the sign and zero restrictions, we also impose narrative restrictions. So we have four. First, we're going to impose that demand shocks have a negative sign in March and April of 2020 to capture the effects of the pandemic. Then we're going to impose that the global supply chain pressure shocks will have a positive sign in March 2011. So this is to capture the earthquake in Japan. Relatedly, we're going to say that in March and the 11 and April 2020 and 21 November 21. The contribution of global supply and the pressure shocks to the GCP I forecast errors are the biggest are the biggest. And then finally, we're going to impose that the monetary policy shocks are the main drivers of the forecast errors of the 10 year OIS rate in January 2015 to capture the announcement of the APP. Okay. So this, this is the, this is what comes out of the B bar. And you can immediately see that in green, this is our identified global supply and gain pressure shock is quite prominent. It's a quite prominent driver of inflation dynamics before the crisis that, you know, consistent with what we saw in beginning of this increasing participation in global value chains, these favorable global supply shocks that were weighing on inflation. And then during the global financial crisis. There was this unwinding of global supply chains. And these shocks had a positive impact on on inflation. And then if you move forward to the pandemic, you see that, you know, around 2021 2022. These global supply chain pressure shocks were the dominant driver of of euro area inflation. Also, of course, with some contributions from demand and from energy price shocks. But I remembered, if I remember correctly, the global supply to press shocks can explain a roughly 40% in the end of 2022. Now, if you look at the impulse responses of your area inflation to this global supply chain pressure shock on the left and domestic supply shocks on the right. We see that although inflation response positively to this domestic supply shock. The response is quite short lived, right? Whereas if you look at the response to the globe supply shock, we see a much more persistent and hum shaped response. Now we don't delve into this very deeply into the paper, but we conjecture that this is about, you know, things like second round effects. The fact that firms might not be able to switch to or set up new global supply chains in the short run in the short run at low cost. So you get this accumulation of effects that cause these shocks to kind of move gradually to the rest of the economy. So the sum of the empirical results. As I said, the Philips curve analysis kind of shows illustrates that global supply chain pressures contribute positively and significantly to your inflation. And the beef or model tells us that these shocks to this, these types of close supply chain pressures were the dominant driver of your area inflation. And also have a very highly persistent and home shaped impact on inflation. Okay. So now to the theoretical part of the paper. As I mentioned, we're going to use a new Keynesian model for two countries. We have home and foreign. The model is very simple, very plain vanilla. So it's like the paper from Benigno 2009. We have households, you know, they consume, they save their work. They consume both home and foreign goods and they hold both home and foreign assets. And whenever they, they, they have foreign assets, they, they face a risk premium. Then we have two types of firms. We have intermediate good firms that are perfectly competitive. So they set price equal to marginal cost. Then we have final good firms that are price setters, but they face like a price adjustment cost a lot of them work. And this is the bit where it gets a little bit non plain vanilla. But so the final goods firm on a goods are produced using both home and foreign intermediate goods. So there is trade and intermediate inputs. And this gives rise to this, this global supply chains, basically. So let me give you a bit more detail. So this is from the perspective of the home country. So there's in home country, there's an intermediate goods firm that produces intermediate good X that a is a productivity shock and is ours work. And then the final good and in home. So why age going to be a composite of this home intermediate good. And this foreign intermediate good, right? So X, H, X, H and X F where five is going to be the elasticity of substitution between these home and foreign intermediate goods. Now, gamma is going to be our parameter of interest. This is going to measure the share of foreign intermediate goods used in the production of home final goods. And this is going to give you a sense of how much this country relies on global value change. So what does this imply when this gamma is greater than zero? It's going to imply that domestic marginal costs are going to be directly influenced by changes in foreign prices or form productivity shock. So Z a star. Right. So there's going to be this additional cost channel that causes shocks to causes foreign supply stocks to immediately feed into domestic costs, marginal costs. So how do we model global supply chain pressures? Well, again, like I said, set basically setting gamma greater to zero implies that this economy relies on global value change. The higher is gamma, the more the country relies on global value change. Now we're going to proxy global supply chain pressures using this form productivity shock Z a star. Right. So if there's a drop in Z a star, it means there is less there are less foreign intermediate goods inputs available for the production of home goods. So we get these close supply chain snarls. And of course, how big these pressures are going to be depends on how large gamma is. Okay. So these are the impulse responses for selection of home variables to this global supply chain pressure shock. Right. So this negative foreign productivity shock for different values of this gamma. The solid line is the case where gamma is zero. And as you can see, in this case, this shock kind of looks like a demand shock in the sense that both output and inflation go up. And kind of the story behind this is that, you know, if there is a foreign supply shock, foreign price go up, then there's this strong expenditure switching effect. That causes an increase in demand for home goods or home output rises and inflation rises and then the central bank titans and consumption falls. But if gamma is positive, right, so the country relies on global value chains. Then we see that this shock acts like a supply shock. So output falls and inflation rises. So the idea here is that, you know, as as foreign goods become more expensive. There is going to be an increase in domestic marginal costs as home firms are reliant on foreign intermediate inputs. And so this will lead to a reduction in demand for home goods. There will be an increase in inflation, more so even than in the case where this country was not exposed to supply chain disruptions. Right, so this is basically what I just just mentioned. Now, because the the reliance on global value chains makes this global supply chart turns it into a more classical supply chart. You also have this classical trade off from under policy between stabilizing output and prices. Right, and this trade off depends on gamma. So the higher is gamma. I said if the economy is faced with a global supply chain pressure shock, the higher is gamma. The higher is going to be discussed channel. And so the greater will be the rising inflation and the dropping output. Right, so if the country relies more on global value chains, the trade off from under policy between stabilizing output and inflation is going to be less favorable. Okay, so how should monetary policy respond to these to this global supply shock. So here I plot again the impulse responses of selection of home variables. For the case where gamma is set to 0.3, which is kind of consistent with the second figure I showed in the beginning. The red desk lines are the ones under a standard Taylor rule. So these are the ones that you already saw. And the blue lines are the ones under Rams optimal policy. And as you can see, optimal policy kind of implies that the central bank should favor stabilizing inflation over output. So output drops by more than under a standard Taylor rule, but inflation is roughly stabilized. Now turns out that the optimal response to this global supply shock also depends on this gamma. So here I plot both the impact peak and cumulative response of the home policy interest rate to a global supply shock under Rams optimal policy for different measures of gamma. And as you can see, up until a certain threshold, if you increase gamma, this response should be more aggressive. Right, so you should tighten more aggressively in response to this global supply shock. Why? Because as gamma is higher, this economy is going to be more exposed to foreign supply shocks. And therefore you want to respond by taming the resulting higher inflation. But if gamma exceeds this goes beyond this threshold, then we have that this tradeoff between output and inflation stabilization becomes worse and worse. And so Rams optimal policy calls for a less aggressive policy reaction. Of course, this also depends on a lot of other parameters in the model. Here I look at different values for this phi, which is remember this is a list of substitution between home and foreign intermediate goods. And essentially, if you increase this elasticity, it simply means that, you know, substituting away from foreign intermediate goods towards home. Intermediate goods becomes easier. And this helps to kind of absorb the adverse effects of a global supply shock, right? And this allows for a more aggressive monetary policy response. The same holds when I vary Eta. I haven't shown Eta, but Eta is the elasticity of substitution between home and foreign final goods. We get a similar, similar result. However, if I then change the degree of price stickiness, then what we find is that the more sticky our prices, the greater will become, you know, the output costs of a foreign supply shock, global supply shock. And this will worsen even more the tradeoff between output and inflation stabilization. So this calls for a much less aggressive monetary policy response. Okay, so just to sum up the theoretical results. What we find is that, you know, if a country relies on global value change, so gamma is positive, then a global supply chain pressure shock acts like a classical supply shock in the sense that it raises inflation and reduces output. And the higher is this gamma, the worse this tradeoff becomes. We find that Ramsey optimal policy implies a tightening of monetary policy response to this type of shock, although this, this, the aggressiveness of this tightening depends in a nonlinear way on different types of parameters. So the gamma, but also other parameters like the substitution of elasticity and the degree of price stickiness. And yeah, so I'm done this space. Thanks. Thanks a lot. No. Thank you. Good afternoon, everyone. Yeah, the usual disclaimer applies that these are my youth. So let me get straight to the point by saying that I found the paper very topical and interesting. And as you saw, the paper is the aim of the paper is to fold first to measure the impact of supply side bottlenecks on your area inflation. And then to derive some lessons for central bankers, how should they respond to such shocks? So the authors, the authors basically find that these shocks are extremely important for your inflation, but they also make the job of the central bankers more difficult as they worsen the inflation output tradeoff. So why do I say that the paper is very topical? So as you all know now, basically this particular type of shock related to global supply chain issues has been in the spotlight since the pandemic now. So we've been talking about it now and also in the central bankers world in policy makers circles and also in the academia you've seen more and more papers on that. But it's also the consumer. So the public at large that has come to understand how important these shocks really are for inflation. So here you have some recent evidence from the New York Fed survey of consumer expectations where the number one driver of the recent surge in inflation in the post pandemic world was identified to be actually issues related to supply chain issues. So indeed, it's something that we have to understand more about. And this is why the papers that we've seen now in this session are so important. But in my view, what I think the paper should stress more is actually the uncertainty related to our understanding when it comes to the effects of these shocks. So I will refer to three aspects. So of course, proxying supply chain pressures, identifying the shocks related to them and also how we deal with the challenges that post pandemic data are bringing. So the first things that I thought about when reading the paper was related to proxying the supply chain pressures. And I think this came out very clearly from this session and also from the first presentation. So basically we are talking about a very complex phenomenon multifaceted. So we are we are talking about problems in transportations due to COVID induced lockdowns or to demand rising much faster than supply could accommodate or even some idiosyncratic weather events. So when incorporating such a phenomenon in your models, we have to take a shortcut now. So we have to use some kind of proxy. This paper is using the GCPI now. So this composite indicator that is focusing basically on supply components. But we've also seen other papers are using different proxies. So we've seen the paper today using satellite data on congestion and container ports. But the literature is still ongoing and people are looking for, let's say the perfect proxy. So plan short and Bernanke are using an index of supply chain problems based on Google searches or some very recent work on using indices based on newspaper data that is out just yet. So let me just compare you've seen basically that GCPI gives different messages to other proxies. And this has major implications in our assessment. And here I'm comparing it to this index used by Blanchard and Bernanke, this Google Trend Shortage Index. And you see that while the measures are correlated, the spikes do not necessarily coincide and the times they also give us divergent messages. And for us practitioners, of course, this is very important when we want to provide some timely information to our policymakers. So I think when talking about this new type of shocks, robustness checks are in place now. And I wanted to ask whether you've considered looking at other indices. Second type source of uncertainty is linked to the identification of shocks. And of course we know in this literature that of course we choose sign and zero restrictions. But in this particular literature, we also have narrative restrictions that are being more and more used and more frequently, let's say to pin down the specific shocks and they relate to specific historical events. So here I'm comparing the discussed paper in the third column with two other papers dealing with a similar topic. And you see that the historical events that were chosen to pin down this particular shock differ. So for instance, actually, the Japan earthquake and tsunami in 2011 is basically used by all three papers to pin down the shock. But other events are used differently. So of course there is also a subjective element, but it would be interesting to know how did you choose the historical events to pin down the shocks and how would the shocks look like if we would be broader, let's say, in the number of events that we would use to pin it down. And the part from this restrictions that are used in the literature, I think another source of uncertainty related to that that came out even more prominently after the pandemic is related to the number of identified shocks. Why do I say that? And why perhaps even more relevant after the pandemic? Because while we were trying to answer actually similar questions as the Dennis and Gauthers, we realized that when trying to explain this abnormal inflation surge, when many indicators had this prolonged spike, you would see some kind of a spurious correlation that would give you a... If you don't control for enough sources of shocks of inflationary drivers would give you a larger contribution from a specific shock than one should actually attribute. So imagine GCPI and inflation rising very fast at the same time without controlling for different, let's say, energy pressures, you would have more coming from GCPI than you would want. And this is driven by this really abnormal spike. So in a model that we identify in a bit of a different way, so it's a larger model that we identified via the Corabilis algorithm where basically reduced form errors are assumed to have a factor structure. We stopped at eight shocks, going further to nine or 10 would give us something similar. And we saw that the contribution of the GCPI or supply chain pressures, the green part. So this green part is the same in the two papers, namely contributions from global supply chain pressures is smaller when you control for more shocks or I don't know. So this was the question for the authors, if you thought about adding one more shock or if you saw that this actually changes. I've seen a preliminary version of the paper without energy shocks and I think that it was definitely different. And actually, you know, this number I identified shocks is still something that is quite ad hoc in this literature, so it's a choice. And the third point that I wanted to make is related to the uncertainty linked to the abnormal post pandemic data. And here I don't refer to these outliers, the strong outliers that were dealt with, let's say by Lens and Trimiteri or by Carriero et al. I'm talking about this prolonged spike in inflation, concomitant with the prolonged spike in other economic indicators that would give us a significant pass through of shocks that we didn't manage to get before. So now we see that some shocks matter and with pre pandemic data, we couldn't find much significance. So the authors augment basically a Phillips curve with the GCPI indicator and they find a significant and strong coefficient. So trying to replicate this basically not similar kind of Phillips curve, I wanted to see how such a coefficient would evolve over time. And basically what I got was that for the entire Eurora sample before the pandemic, the coefficient was very close to zero with a little movement. It is only when adding this post pandemic observations that you get some significant effect coming from that. And this is a smooth estimate of a time varying model in a filtered one you would see even clearer. But in a way it is it is basically a judgment call. Now it's a stance to make which sample you want to use and Blanchard and Bernanke very nicely put it and they take the stance in the recent paper by saying in order to include meaningful variation in the effects of sectoral shortages. We estimate the baseline price equation over the full sample. Why exactly to get this effect of the short sectoral shortages in. But it also shows us how difficult it is actually to pin down the sources of inflation in real time exposed. Of course, things look a bit more clear, but in the real time things are very difficult. So I don't know if you also observed some some instabilities over estimating on different samples. So let me wrap up a very relevant paper and it stresses now that in this deeply connected world. We have to take a more seriously external factors when modeling inflation such as problems along the production chain. So it's basically along the lines of a paper by Christian Forbes in 2019 when she was saying that our models are basically putting a very strong emphasis on domestic drivers. And also actually it is not so trivial. It's not easy how to pin down the impact of these shocks, especially in real time. But our central policy makers actually have to take decisions now in real time and and what the authors also nicely show is that. The optimal reaction to this supply chain bottles next shocks depend on the integration in the global value chains in a non linear manner. Now, so the more integrated actually the less aggressive you have to react due to the worsens of this inflation output trade off. And this actually only adds to the challenges faced by the policy makers. Thank you. Thanks Elena and maybe we can take a couple of questions and then like if there is some. Yep. Please identify yourself. Hi, this is Catalina Martinez Hernandez from the ECB. So, I was a little bit surprised to see that the contribution of the of the global supply chain shock was very large in the low inflation period. So I was wondering if you can give us a little bit more intuition why is this the case. And also something in line with what Elena was saying, maybe one shock that is missing to explain also this period is shocks related to the labor market. So I was wondering if you were also considering further robustness. Thank you. I mean, when thinking about global value chains, I thought we, I would have heard the word exchange rate much more often. Meaning the exchange, especially since I think of us as a global shock. We also had an old literature on global value chains that with global slack and how will demand effects in general, the slope of the Phillips curve, not just for the domestic output but also global one. Neither of you mentioned neither the exchange rate as part of the Ramsey policy nor the extent to which global stock and going back also to the discuss it on how the slope of change. I mean, this was a global supply shock at the world level as opposed to an individual country. I would think that would matter somewhat. So if you could say something about the exchange rate and global stock, I think that would be helpful. So, first of all, thanks for your very nice discussion. Yeah, so you had three points. First on the uncertainty on the proxy for the supply chain pressures. I think Lee's presentation was all about this. And the point is well taken. It is on our to do list to also consider other measures of global supply chain pressures, including indeed the Benanke Blanchard indicator. So this is something that we definitely need to pick up as well as on your second point, the uncertainty on the identification, adding more shocks. So we recently added the energy shock and that that already changed somewhat the results quantitatively. Yeah, we are we're also going to, you know, think about other kind of restrictions trying to use, for instance, the war in Ukraine for identify the energy shock. For instance, use of post COVID data also kind of complicates things. As you also showed, is perhaps necessary to, you know, to properly identify the shock, you know, leaving out COVID. And if the end these extreme events may leave you to, you know, have a very unmeaningful effects. But we can definitely also emphasize this more in the paper. Yes, then Catalina's question. So the low inflation period we do do and see indeed see some some impact from this global supply chain pressures. So let me think. Yeah, so this is so we had we kind of relate our narrative basically related to the figure that I showed in the beginning on development of global value chain participation. So before the global financial crisis, we saw this increase in participation that we link to more favorable global supply chain pressures that put downward pressure on inflation. And then as following the global financial crisis, firms started to reconsider their global value chains. And some of these global value chains started to unravel. There are more and more positive contributions for the global value chains shocks on inflation. But I also have to admit, I was also surprised that this these contributions are so large, basically, although they do somehow fit into Forbes paper. That shows that, you know, you need kind of external factors that can explain why perhaps monetary policy has been so right has been so difficult to kind of control inflation during that period, because of these offsetting effects. And, yeah, on your labor market, that's good idea. I mean, I haven't thought about it, but it would be interesting to have a monthly model, but it would be nice to if you somehow can can also incorporate labor market shocks. And on regards question. So we, yeah, we do identify also real exchange rate shocks in the different model. And in the new case model, we also spent a little bit of time also mentioning that this additional cost channel that arises because of these global value chains gives an additional role kind of for the real exchange rate on from domestic marginal cost, basically. So that that already is there in the field script if you have an open economy model, but this gives you an additional channel basically. I don't know if Leo also wants to comment on this. Yes, so thank you Ricardo for the question. I have to say that was a fantastic one because in a very, very preliminary version of this paper we actually have exchange rate and BOP stuff in the SVR. But it's just because for the simple model that we build to to get those identification restrictions, we really don't have a part on the exchange rate. So essentially, we have to let the exchange rate on restricted in the SVR estimation and as can as can imagine in terms of the SVRs, the impulse responses functions, we don't really get a very meaningful results. Especially now, you know, at that time, we were using the, you know, the, the Bayesian method by Jonas Arias. You can imagine those confidence bands don't really give us any information. But on top of that, we actually have a following paper on. So this was not something that we have done in this paper, but we wanted to look at cross country differences in terms of the effects of supply chain disruptions. Right now we are looking at China and United States. And for China, we're actually trying to disentangle this exchange rate depreciation coming from the lower grade and supply chain disruptions. And, you know, you know, how each of them playing some roles in this, I would say right now for the aggregate implications for the United States and China, but still working on it. Thank you. We have time for a couple of questions. Well, if not, we can have a longer break, but thanks a lot. All of you for for your presentations and I think we are back at for 15. Thanks.