 Good morning, everybody. We are going to have, you know, this first presentation that is going to be dedicated to real convergence. In order to do that, we have a paper from Jan Imps, Jan is professor of the Paris School of Economics and the New York University in Abu Dhabi. And I think that you have co-authored the paper with Logan Paul's lecture in the University of Sydney. And for the discussion, we'll have seven Calendki Otkin from the University of Maryland. Well, real convergence is a key variable in economics, and especially, you know, in the Monetary Union. The launching of the Euro was about nominal convergence. And well, the party line in those times is that nominal convergence could pave the way for real convergence. But perhaps we cannot overlook that the determinants of real convergence are real factors, mainly total factor productivity. So I think that these are the kind of things that we are going to discuss now. So, Jan, you have between 20 and 25 minutes to make the presentation of your paper. Thank you. Thanks very much. It's a privilege to be here. This is a part of a project I have with my co-author, Laurent Pauls. And what we're trying to do here is to take stock on 20 years of convergence. And I mean, a little bit precise about what I mean in terms of convergence. I'm going to be talking specifically about two types of convergence. One in GDP that President Draghi actually alluded to already. Convergence in GDP, that is the reduction in the dispersion of GDP growth rates, which is obviously of paramount importance for the conduct of monetary policy. First, because it's one of the criteria for an optimal currency area, cycles synchronize, and that makes monetary policy easier. And the second, because of the cohesion and homogeneity that creates within the union. Convergence in consumption, which is intimately related with financial integration and risk sharing across member countries of DMU and therefore is intimately related with welfare and potentially even issues of inequality. But there are many other forms of convergence, convergence in labor market outcome, labor productivity, etc. In my talk, I'm going to focus on the first two. And as a matter of fact, I'm going to dedicate the bulk of these 20, 25 minutes to discussing convergence in GDP, the first point. Time permitting, I'll say a couple of things about consumption as well. So, one very, very simple, straightforward way of looking at this issue is to compute the object that you see on top of this slide, which is a measure of absolute differences between growth rates of GDP, that's the measure to the left, and of consumption growth rates, which is measured to the right. These absolute differences can be computed for the whole cross-section of pairs of countries, and are going to capture how dispersed growth rates are of GDP or of consumption. And the simplest way to think about convergence or lack of convergence is to simply investigate whether they exist a trend, an upward trend, into these dispersion measures. Now, these are absolute differences, they are absolute values, and there's a negative sign, which means when these things go up, they go up towards zero, and so that characterizes convergence. And so what I've done here is I've just run a simple linear trend regression, to establish that both of these measures, over the 20 years, the first 20 years of the EMU, have actually increased. There's a trend increase that manifests both in GDP convergence and in consumption convergence. The trend is a bit more marked for consumption, interestingly, than it is for GDP. That continues to be to over a period, separated roughly the first 10 years and the second 10 years, and you can see it's there in both periods. Convergence is manifest, it's salient in the data, at least as far as that measure is concerned. And so that begs the question, why have we observed these convergence? So let me talk first about convergence in GDP, and by way of motivating the question we want to ask, let me just refer to an enormously large literature that has taken interest in that object. Now, why is that interesting? Why is that important? Why is it an important question? It is because it's key to, again, the ease with which a monetary union can function. A few people would doubt that a monetary union actually stimulates trade. And so that's the first arrow I have on this slide. There's a little bit of disagreement about how much it stimulates trade, but it's pretty clear that it does affect trade. So more trade in monetary unions. And there's also a few people in this room or elsewhere that would disagree with the fact that more trade actually creates convergence. So that's a very well-known channel going from monetary union to trade to convergence. And then there's this feedback effect that when GDP actually converge, that actually facilitates the cohesion and the sustainability of monetary union. So one of the things that I want to really talk about today, this morning, is the second arrow, the relationship between trade and GDP convergence. Empirically, has been established on the basis of observed trade. So on the fact that activities, sectors, countries that trade more and we observe that trade, exports plus imports relative to GDP, are more synchronized. That's an exceedingly famous paper by Geoffrey Franklin and Andrew Rose in the late 90s that established that. But of course, deep economic integration, akin to what the EMU is trying to achieve, involves more than just more trade. It also involves that non-traded sectors, particularly service sectors, would actually cater for traded sectors. So even though business services do not trade with each other, even though lawyers or accountants do not trade across the border, to a small extent, clearly they tend to cater for companies, for sectors that do trade over the border. So that is presumably one of the characteristics of deep, deep economic integration. When non-traded sectors that do not trade directly actually cater for very open activities and very open sectors. So that measure presumably correlates to a certain extent with observed trade, but it is at least conceptually different from observed trade. So what I want to do is to compute this measure of connection between sectors and then between countries and try and distinguish its effect on convergence from that that is the well-known one created by observed trade. So how do I, is it possible to come up with a measure like this? Let me just use a very simple diagram to illustrate what I have in mind. This is what I have in mind, you know, in both sides of the border, you have non-traded sectors, say business services, that actually supply inputs to manufacturing sectors, traded sectors. And the trading that we observe is between manufacturing sectors, but it is not between business services, yet business services are actually quite connected across the border, but sort of one step removed because they are very connected to open sectors. So the name of the game here is to distinguish a value chain akin to what I'm showing you now, which is one that involves one border crossing, or for that matter more than one border crossing, it can go back and forth across the border. That's also what deep integration would be about. Distinguish that value chain from one that actually does not involve crossing a border. And here is an example of that, that is purely domestic. The sort of connection measure that I'm interested in is one that would capture the black path here and filter out in some sense the red path here. And that's going to be something that is going to be reasonably easy to do using input-output tables that capture these vertical trade between different sectors. Now input-output tables actually tell us a lot of information, often quite messy and actually quite messy to expose as well, so hence the triple summations. What this complicated expression actually tells us is quite simple. It tells us that gross output in a given country i and a given sector r is going to serve demand, final demand in a variety of different ways. It can serve it directly, and that would be the first f's that you see in both of these brackets to the left. The fiir in the bottom basically says that this good i is actually consumed through final demand in the same country and in the same sector. The sum, so the sum of the fijr, it means the same thing. It means that that good serves final demand but across a border. So this is crossing a border but in the form of final demand. So that's the type of trade that we used to think about, it's trade in final goods. But of course there's also second order linkages, that is the second set of summations here where at the bottom here what you have is the possibility that that good r in country i is being sold to another sector s in the same country and it is that other sector that actually serves final demand. You can have that in a purely domestic sense that's the bottom bracket or in an international sense that's the top bracket. The top bracket captures the various ways in which at the second order that good r can cross a border, it can cross a border as an intermediate good across sectors or as an intermediate good within sectors. That would be a second order link towards the distance from final demand. And so on and so forth. There's a third order, fourth order, fifth order, etc, etc. So what I'm trying to do here is to disentangle basically to isolate the first top bracket from the bottom bracket. I want to isolate that component that it really captures these intermediate inputs crossing borders. Now that's actually a pretty difficult thing to do even though these data is available because these are infinite sums. Thankfully there's a very influential paper by Antras and Thor a couple of years ago that slightly amends the objects that I've just introduced to you by if you see there's a 1, a 2 and 3 now there weights that actually captured the distance to final demand so that weighed these distances by the number of steps for each elements in these brackets. Now this object actually can be computed very easily using input-output tables and that's the formula you see at the bottom there. The formula at the bottom there involves a which is a simple input-output table, a world input-output table that we have data on and f which is a vector of final demand. The bottom line here is that I can easily compute the sum of these two brackets by using a world input-output table and a vector of final demand. Now by analogy to this result I can also compute the second bracket which is the one that's focused on domestic linkages only by specializing the matrices a and f onto purely domestic input-output linkages and purely domestic final demand that's the notation that's at the very bottom of the slide. Now having done this I have an expression for the left hand side what I've called u and I have an expression for the purely domestic component so I can back out for any sector in any country the propensity in some sense the intensity with which that sector exports across the border directly and indirectly at first, second, third, fourth order etc etc. Now I'm going to call this measure a measure of export intensity bearing in mind it's not directly observed, it's not a measure of trade. It's a multilateral so far multilateral measure of export intensity in the sense that it captures how closely, how export intensive, how closely linked to all of the country's partner given sector R is in country I. So let me show you what that looks like. So what I've done here is I've done that computation for China, EMU, EU not EMU and the U.S. China is in black, the EMU is in green, EU not EMU is in purple and the U.S. is in red. So you can see export intensities have very different trends between China and the other three groups. China has an upward trend so that makes it a very open economy in the sense of this export intensity being quite large as in lots of sectors in China are geared towards exports but you can see that post-2007 the measure actually falls back down which is presumably consistent with the rebalancing of the Chinese economy towards its domestic activity. And what's interesting is that at the beginning of the euro China and the EMU are at similar levels and at the end of the euro again China and at the end. Today China and the EMU are at the same level. So there's a sense in which exports intensity in the euro area is comparable with what it is in China. By the way what I mean to say here is that the export intensity of EMU member countries with each other is comparable with the export intensity of China with its trade partners in general. What's also interesting is the gap between EMU green and EMU green and EMU not EMU the purple one. So there's an EMU effect in the sense that export intensity between EMU members is larger than it is between EMU members out of the EMU. With all of that the U.S. is actually at the bottom. It's actually a country that is largely closed by according to that measure. So that's the first thing that suggests this measure is potentially interesting and says something else than just pure observed trade. The second thing that I want to do is to try and bilateralize this so that I can say something about bilateral connection between sectors across countries. The way I'm going to do this is I'm going to specialize what I've just showed you before rather than using a matrix an input-output matrix of China with the rest of the world. I'm going to take input-output matrix of France with Germany and that's going to tell me the export intensity between France and Germany. It's going to tell me how sectors in France, sector R in France is actually exporting intensely to Germany and that the way I'm doing this is by introducing IJ versions of all the relevant matrices that you see on that slide. And that gives rise to a very easy way of measuring bilateral export intensities between sectors and between countries. Now one of the reasons why this is interesting is that this can be computed for non-traded sectors. In particular it can be computed for services, business services. I'm actually able to say something hopefully meaningful about how connected across the border the typically non-traded sectors are. And I'm going to call this BI for bilateral intensity. And let me show you this again by way of documenting what these measures look like. So these are pairs of sectors between emu member countries that's in red, between the US and its trade partner, it's in blue, and China and its trade partner that's in black. And you can see that the overwhelmingly apparent result is that the red numbers are much, much larger than the blue or the black numbers. Emu member countries are highly integrated bilaterally in terms of sectors actually being connected with each other. You can notice so manufacturer with manufacturer has very high numbers which is not too surprising. These are connected traded sectors. But you can see that services, business services here have sampled a few. Retail also take relatively large values within emu countries. So this is an index of how closely connected non-traded sectors are across the border within the emu. You can see the numbers for business service with business service are actually not zero. There is some connectivity between these because they in the emu tend to serve sectors that do trade with each other. Now obviously the 64 trillion dollar question is how whether that explains convergence. So the way I'm going to do this is by running the regression that's up there which is on the left hand side the measure of convergence in GDP except that this is now measured at sector level. So I'm going to look at how sector R in country I is converging with sector S in country J. And I'm going to try and explain this with the bilateral intensity measure just described which is this bi variable right to it. And I'm going to run a horse race with trade. Now trade here is basically the effectively traded the amounts and value that is traded between these sectors and these countries coming directly from the input output table. So these are directly observable trade between these sectors. So it's defined in a usual way which is the amount that is being traded that's disease normalized by value added. Now this is a huge dataset because I'm going to look at all bilateral relations between all the sectors in the in this case 12 core emu countries. So what I'm going to have is 12 countries times 50 sectors times 50 sectors because all the bilateral relationships times 15 years only have 15 years because that's the input output data that we do have. So that gives me a dataset of 2.5 million observations whose purpose it is to try and explain the dispersion in convergence in sector value added growth rate using this bilateral intensity measure. So let me show you the results. These are the results. So again the idea here is that I haven't said that but I have a battery of fixed effects. So in particular everything here is estimated within over time. It's an increase in bilateral intensity and I'm looking at whether that increase in bilateral intensity actually explains increasing convergence or convergence. So the first line is the one of interest. The first line captures whether that's significant or not and it's systematically significant and positive and I'm adding different fixed effects, different combinations of controls really and also trade, the directly observed trade. So a couple of things are interesting. The first one is that the trade coefficient is actually unstable to the inclusion of fixed effects. So it's actually very hard when you use observed trade at the sector level to explain convergence in value added growth at the sector level. You actually cannot do this in any robust way. However with that bilateral intensity measure you actually can do that. You can see that the coefficient is systematically significant and positive and economic significance is actually quite substantial. It's roughly three times larger for bilateral intensity than it is for the trade. So it is a relevant verbal. Another way of looking at the same question is to sort of look at specific pairs of sectors, it's still within the emu, specific pairs of sectors as opposed to this is the whole cross section of all sectors. Now I'm here specializing on some pairs between the sort of three key activities in the economy. And so you can see again that what's remarkable with this bilateral intensity measure is that it really explains very well the correlation between manufacturer and services, services and services, agriculture and services. It's actually explaining quite well the connection between services and other sectors of the economy across the border in a systematically significant way. And those are the last five columns in the table. Compare this with trade, observed trade does a really bad job. It's a very unstable job. And that's not surprising because once again observed trade is just probably for some of these non-traded activities, a small part of the actual linkages between these sectors across countries. Okay, so the last question is how does that aggregate up? That is how much does the ability of this variable to explain correlation at sector level between countries actually aggregate up to explaining aggregate cycles, which is eventually what we're interested in as micro-economists. Now absolute values really do not sum up very nicely. They're quite awful. So what I'm doing instead here is I'm using a quasi-correlation measure, which is the object that's written here, which can be computed in every period reasonably easily. And that actually does aggregate quite nicely. It's the last double summation at the bottom of this slide that tells us that QIG T, the aggregate correlation, quasi-correlation between cycles, is a weighted average of all the sector level bilateral correlations. Okay, and so now the name of the game is how much of that QIG T change in QIG T? Am I going to be able to explain using the bilateral intensity measure I just described? Okay, by the way, using the quasi-correlation measure you get the same result that convergence has happened, that is called the distribution of these correlations at the country level has shifted to the right. Countries have become more synchronized, first 10 years versus last 10 years. Okay, so how am I going to do this? Well, I'm going to fit a model at the sector level, which is the model that's up there, and I'm going to get fitted values of the quasi-correlation at sector level, QIG RS, with a hat. That's my fitted value. The first one is the fitted value that's predicted by bilateral intensity. The second one is the fitted value that's predicted by trade, just to sort of run again a horse race between the standard explanatory variable for correlation that trade is, and once I have this fitted values I'm going to aggregate them up using the formula I just showed you, those are the two summations at the bottom, and then I'm going to get a fitted predicted aggregate correlation as implied by bilateral intensity as implied by trade, and I'm going to compare this with the actual measured bilateral correlation. Okay, so first thing, this is my fitted model, so it is, so these are regressions not unlike the ones I showed you before where I'm trying to fit bilateral sector correlations on bilateral intensity and trade, and I'm going to take my predicted values from this model. Here's what it gives us. I guess that's a headline result. This regression is exploring the ability of the bilateral intensity variable to explain from a sector level, aggregate correlation versus the trade variable to explain at a sector level, from a sector level aggregate correlation. So the first column tells us that the model that is only fitted on bilateral intensity does a pretty good job at explaining the increase in correlation. The R-square is 20%. That basically says that bilateral intensity explains 20% of the increase in aggregate correlation, and that's, again, built up from a model that's fitted on the sector level. There's no macro shocks here. There's only sector shocks. Compare this with trade, just to give it sort of a yardstick of how big or small that is, and for trade you get an R-square that's a little bit smaller, and I guess if you put the two of them, which is the tempting normal thing to do, what you get is its export intensity that seems to come out on top. So at the very least, that measure is on par with the measure of the standard measure of trade intensity that we observe in terms of the explaining GDP convergence. Now I have two minutes, and in two minutes, let me talk a little bit about convergence in consumption, which is the other thing I promised I would say something about. Now here, this is actually a very charted territory. I'm sort of revisiting in a slightly different way an enormous literature. The idea here is to run risk sharing tests, that is, tests that investigate whether the growth in consumption can be explained by financial integration, and financial integration. These regressions are well known. There's one in the middle here. This beta that you see here is going to measure the extent to which growth in consumption can be unhinged from idiosyncratic income shocks. It's with financial integration that I can consume in a way that is not dictated by local income. And then estimates of beta should be zero under perfect financial integration. And you know, if financial integration matters, you'd expect the interaction which is in the bottom regression here, this beta 2 to be negative. More financial integration, lower coefficient. I'm going to measure this financial integration measure using the number of financial service action plan directives adopted by each country at time t. This is directly inspired from my discussant. And ask whether beta 2 is negative or not. The slight wrinkle that I bring to this approach is to sort of fit it to the bilateral measures that I've discussed throughout these 20 minutes, and actually look at whether convergence in consumption can actually be explained or related with convergence in GDP. And that's the coefficient delta that you see here. And again, same idea, you'd expect delta to be zero under a financially integrated area. And the question is the same. Do we observe delta to be closer to zero when financial integration is more prevalent? That's the delta 2 I have at the bottom there. Again, how do I measure financial, bilateral financial integration between two countries? 7M has this measure, which is basically looking at the number of common FSAP directives adopted by countries I and J. When you do this, you get these results. And these results actually, after I found them, I discovered are pretty well known and reasonably conventional standard. What that tells us is risk sharing is imperfect, quite imperfect. These coefficients, beta on the upper panel, delta on the lower panel, are really far from zero. But if you look at the interaction, which you see is that in the earlier period, 99- 2006, as a negative coefficient, which suggests that financial integration through this FSAP directive is actually achieving a significantly larger degree of financial integration, whereas the opposite happens in the more recent period, the post- Lehman Brothers crash from 2008. That's true across both approaches. Let me conclude because I'm short on time. So I'm doing two things. Well, one really big thing and one really small thing in this paper. The first one is to propose a novel way of measuring convergence or synchronization in GDP growth, which has to do with a measure of connection between sectors and also between countries that actually is not based on observed trade, but rather is based on input- output linkages and the proximity of two sectors across the border through their input- output linkages. What's interesting, what I find is that that does explain convergence quite substantially at sector level. It trumps the typical explanation coming from direct trade. And in terms of how much it can explain of the aggregate change in correlation, it's explaining about 20%, which is at least on par with the standard explanation on trade. The second thing and smaller thing that I do is consumption correlation and what seems to be happening is that the directives, the FSAP directives did have an effect on financial integration pre-global financial crisis, but not post financial crisis. And let me stop here. Thank you very much, Jens. Now, Sevnem, it's your turn. Let me start by thanking the organizers for inviting me. It's a privilege to be here and celebrate the 20th anniversary of Euro with this distinguished crowd. This is a topic that is dear to my heart. I spent a significant chunk of my adult life thinking about European integration and I think it's a great paper. So I'm going to try to highlight the key points and bring another perspective to the table. So let me start by stating the key facts that the paper documents. So the paper shows that there's convergence in pay-wise growth rates of GDP and consumption. The explanation for this convergence is the value chain. So the idea here is there are sectors that are not trading with each other across border, but they are linked to the sectors that are trading and these sectors help convergence both at the sectoral level and at the target level. And the last fact the paper shows is there is also convergence in consumption due to financial integration. I think this is an extremely comprehensive paper, very nice contribution. It's an amazingly rich canvas of data that the paper is providing us with and it introduced this new trade measure, the main contribution of the paper that really helps us to understand how deep the European economic integration is. So my comments are going to be on first discuss the measurement of GDP convergence, focusing on the role of financial integration in this process, something Jean didn't have time to go through and then I'm going to link this to total factor productivity convergence which is another real variable we do care about when we talk about real convergence, then going to talk a little bit about consumption convergence and if I have time I'm going to talk about importance of having a framework to interpret the result and to drive out policy implications. Let me start with measurement. I would like to distinguish the two concepts GDP convergence and GDP synchronization because I believe what the papers is doing is measuring GDP synchronization which is an amazingly important object for an optimal currency area as Jean pointed out. You can use two metrics if you would like to look at GDP convergence. The first one is the standard metric that comes from the growth literature. It's based on a neoclassical growth model known as beta convergence. This metric is about whether or not poor countries and regions grow faster so they will catch the rich countries or rich regions. So that's the standard one. That's what we understand with real GDP convergence. There's a second measure which the papers is adopting that comes from the international business cycle synchronization literature and that measure is synchronization of GDP fluctuations. The authors measure. Obviously this is an extremely important measure in the context of optimal currency area going back to the influential work by Mandel on this. We know that desirable economic integration centers on the degree of synchronization of GDP fluctuations across borders and we later understood that economic itself, economic integration itself affects the degree of synchronization of GDP fluctuations. So if we want to put this in a framework let's first start with the standard measure and then go to the second synchronization measure. So the standard measure is a very simple measure and that measure is going to give you divergence in GDP for EMU 12. You know you may not put that much emphasis on this because at the end of the day you are looking at 12 countries and the measure is a simple regression of growth rate of those countries on their initial GDP per capita levels. When you do that regression you will get a positive correlation. The beta convergence is about getting a negative correlation if you want to say poor countries grow faster. So here the first panel shows that this didn't happen. The literature does this conditionally, meaning conditioning on the determinants of the steady state where countries are converging to such as you control variables as education rate, investment rate for tilt rate. When you do that in panel B that's the conditional beta convergence figure that shows neither a divergence or a convergence result so no relationship between the initial income levels in 2000 and the subsequent growth in the next 15 years. Again this is 12 observations you may not want to do this with 12 observations you may want to look at regions. In fact there is a very active literature in the US that looks at the US states convergence. Now when you do that with regions these are not two level regions of Europe you do get convergence. The first figure shows you that there is convergence among regions within EMU countries that says poor regions grow faster. This is color coded so you can see that you know purple, orange these are the regions of Greece, Spain, Netherlands, Ireland, Portugal they are kind of these top regions they started poorer they grow faster. Now when you look at the second sample after 2007 this breaks down again you get the divergent result that's the second chart but that might be because these are unconditional correlations between the growth rate of regions and the initial income per capita level of regions maybe a better thing is to look at the conditional correlations and that would be this table summarizing these results where the beta coefficient is going to be the coefficient on the initial regional GDP per capita where we are regressing the growth rate of the region and you see the first column gives you divergence but the minute you control the author's favorite measure export intensity in the second column you get a convergence result across the European regions and then of course this is a country level variable here I'm controlling you can also do it with a country fix effect in column 3 for the entire sample period and column 4 and 5 before and after the crisis you always get regional convergence. So what is this saying is if you just use the standard beta convergence measure from the growth model you get a no convergent result for 12 countries in the sense that poor countries didn't grow faster but you definitely get a astounding guess for the European regions that poorer regions did grow faster than the rich ones and this is actually part of the whole cohesion framework it is very important for the economic integration within the EMU. Now let's look at the second measure which is the author's measure that's the synchronization of GDP fluctuations clearly the GDP became more synchronized the growth rates became more similar during these 20 years why is that is the case the first thing we would like to understand is the fact that this measure of GDP convergence which is the synchronization measure is endogenous to trade and financial integration obviously authors realize this and their central argument is about endogeneity of this measure to trade their explanation is that trading sectors are going to have more similar growth rates they are going to have synchronized GDP over time and that's going to give you a good convergence at the country payer level let me put this in a general framework since this has been the the box and also of the optimal currency area literature this is a figure reproduced from my 2001 paper in GE but we try to do this in this figure and in that paper is let us go and summarize the theoretical and understand if a currency area can be an optimal currency area exposed even it wasn't an optimal currency area ex ante so here you will see that there were several papers that argued that you know if countries have similar technology similar structural policies more trade this is going to give them more GDP synchronization their growth rates are going to be more similar financial integration is going to work in the opposite because financial integration is a trade in different goods so that might give you less GDP synchronization so the authors adding another box there through trade integration instead of just doing the intra trade you know link to the value chain they are saying let's also look at the part of that value chain that just don't trade with each other and is it the case that that gives us more GDP synchronization and the answer is yes let me skip this if we run their regression they graciously provided their data to me so I can do few experiments with their data so the first column here is their regression at the sector level so this is again regressing the negative of the difference of the growth rate so it becomes a measure of GDP synchronization on export intensity and you see in column 1 that export intensity has a very big positive coefficient together with you know trade intensity which is not significant so this is their I totally believe in this result and I think it's a very important result that it shows you that the sectors with high export intensity have similar growth rates now you can add financial integration to this regression and you are going to get a negative coefficient at shown with blue in the first column why is that because financial integration is going to make sectors and countries diverge from each other because of the capital flows and specialization patterns but the capital is still going to be extremely sensitive to shocks for example column 2 is going to be a specification adopted from the IMF VO chapter in 2003 where instead of using country time fix effects in column 1 you remove them and you put a fix effect for the crisis here a crisis dummy that is the first line the coefficient in black bolt and it's a big positive right why because during crisis of course the growth rates move together down across and by the interaction term of each of the variables that affects synchronization like export intensity trade intensity and financial integration together with crisis and you can see that the effects are going to be different in normal times and crisis times the last column does the same exercise with aggregate data for country pairs and you can see that for example financial integration makes the growth rates diverge during normal times but then converge during crisis times and export intensity is going to do with the quasi correlation measure the key here is that all these measures capture similar things so I'm going to show you three plots here the first one is plotting the authors the growth rate differential measure against the standard pairwise GDP correlation the second one is the same growth differential against the quasi correlation as John showed you that they use also to link the sector to aggregate and the last one is the quasi correlation GDP correlation so these shocks policy changes and the trade and financial integration so it would be useful to kind of see them in a broader framework it will be also helpful to understand the nature of these measures when we want to think about total factor productivity convergence now the paper clearly makes the case that sector level GDP become more synchronized because the sector level growth rates differences go down over time and that's because of the degree of export intensity of these sectors what does it imply for the total factor productivity convergence on the one hand if these sectors although they are not trading with each other if they are sharing a similar technology that will bring you a total factor productivity convergence another story might be the fact that there might be misallocation of resources within those sectors across firms this is the story we pursue in my joint work with Gita Gopinath Lucas Karabarbonis and Carl T. 2007 where we only focused on manufacturing sector and the southern European countries where we show that the extent of the misallocation of factors actually give you TFP divergence within the manufacturing sector here I'm going to plot you the aggregate TFP so the first panel is going to show you four countries Spain in black Italy in orange Germany in red and friends in blue starting in 1999 everything is normalized to one you see TFP divergence across these four countries when you look at EMU 12 and separated as north and south you also see a divergence to a lesser extent getting a little bit you know higher after the crisis it's important to understand how these two forces in track because clearly if the export intensity affecting many sectors prevails at the end that is going to be a force that is working against the misallocation of sector because that's going up to aggregate TFP convergence. Okay so let me say a few things about consumption convergence and at the end of the day as a European Union this is the most important thing from a welfare point of view right so we care about consumption convergence and as the paper correctly also puts it out EMU countries might be having different shocks and this was emphasized both last night dinner speech and today's Chairman Dragos speech countries are having different GDP shocks right if you are in a union where countries face different GDP shocks what matters at the end if financial integration smooths out these shocks so the citizens of EMU is going to have smooth convergence consumption alters measure this with the standard risk sharing regressions at the aggregate level and the bilateral level the idea behind these regressions is very simple you try to understand how much consumption commutes GDP okay consumption smooth the headline result of the paper when they focus on consumption convergence is that financial integration is great because it's smooth out consumption fluctuation before the crisis but it made it worse after the crisis there before the crisis results are very robust and it's very similar to what has been found in the literature so far I would argue that there after the crisis result is weaker why well it's significant in the aggregate regressions but not be the reason underlying this let's start with their measure their financial integration measure is based on the FSAP director's I mean this is a great measure you know since I introduced it I love this measure obviously so but let's try to understand what the measure says and you know what it might imply during a crisis this is an exogenous measure of course it's great it's about like two countries passing the law and it's extremely policy relevant when we originally introduced this 10th anniversary of the euro at the conference at ECB that this measure is going to help you to disentangle the effect of single currency from the financial harmonization post on financial integration so it's clearly major relevance for the process of further integration but at the same time this measure is about the laws right what the authors finding is that consumption commuting more with GDP in pair of countries that pass the laws at the same time okay but this doesn't mean these countries changing consumption they might pass the law but it doesn't mean they are yet deep the financial integration integrated the factor that that law implies in fact the paper we wrote for the 10th year of the anniversary shows that financial integration help consumption smoothing tremendously but there's a different role played by cross-border liabilities and assets making the job which is the essence of the banking union and capital markets union and there's a lot what is going on during the crisis okay so let me conclude because I'm out of time just let me say one thing I think it is also important to have a framework to be able to link the sector level result to accurate result the idea is this right there are trading sectors not trading sectors helping the convergence how do we link export intensity something that doesn't vary over time as you can see here it's it's flat for many countries but we are trying to explain something time convergence over time and to also help to have a framework in terms of understanding right counterfactual EMU 12 GDPs are more synchronized relative to US and China for sure but does it mean US and China are subject to different temporary and permanent shocks they have different degrees of trade and financial integration or EM-12 are integrated more so a framework will help us to also you know compare the findings to relative to the market that focuses on GDP and consumption convergence to very important concepts for the economic integration in EMU and it provides a view of integration that is much deeper than the standard trade integration because of the effect of the value chain and the sectors that are linked to the exporter sectors through the value chain. I believe it is important to note that financial integration might cause divergence in GDP during normal times but it is even at the 20th year the banking union you know just you know getting full so it is going to get more and more moving forward so this is an important thing for us to realize and the open question on the table is that if the non-trading sectors converge due to their link to exporting sectors this is the value chain link why the aggregate GDP didn't converge in the terms of the standard measures this can be just a data issue for the sectors yet linked to the exporting sectors then it will have a different policy implications so it will be important to sort this out. Thank you. Thank you very much. I think that we have between 10 and 15 minutes to open the debate. Thank you very much. Richard Baldwin Graduate Institute of International Studies and voxcu.org. First of all great paper I was an innovative approach very glad to see it what I wanted to do is provide a little bit of a trade perspective on where we are in these kinds of measures so two things which you didn't mention which may help clarify why it's so important to do it the way you do it is this difference between value added trade and gross trade because trades crossing borders several times the headline the standard measures of trade have become because what you really want to know is what is the value added in the physical products crossing the border and where that physical value added came not just the measures. So it's a big step forward to use these multi product multi nation in an input output tables to figure out where stuff actually comes from the second trend which you didn't mention but I'm sure you're aware of is called the true value added of manufacturing sectors is coming increasingly from services that are added before fabrication or services that are added after fabrication and so you get this what you picked up is countries are more integrated than would seem simply according to the value added trade. So we even have a name for that we call it mode five services trade or embedded services trade now what I would like to suggest and this is goes to me about the numbers is we've actually taken another evolution in trade using the value added tax data to figure out who's connected to which firms which are then actually exporting and it's only done right now in Belgium but the national bank of Belgium has all this data and they can see which firms are selling to the firms that are then actually exporting so you get kind of a friends of a friends of exporters approach to it many many more firms are actually exporting indirectly but with you have the value added tax which is the complete universe of transactions you can get a much more defined picture now right now only in Belgium can you do that because it's the central bank that has the VAT data and the trade data but in principle you could connect the entire thing so maybe if we take this seriously we could do a whole new project at the ECB like we did the value added tax at the firm level to the behavior of trading and then you can really figure out how connected different sectors are so I would just make that as a suggestion great paper too bad I didn't think about it first but it's really really nice thank you. Thank you very much Jen. So do you want me to reply to Richard so thanks very much I you know what I had in mind was those papers that do that at the firm level but I just didn't have the data so doing it at the sector level at global in a global framework is what I chose but I agree second best I mean gosh if the data you describe could be collected for in new countries that would be fantastic to be honest to ask this question beyond Belgium and you have value added trade versus gross trade I think you're right it'd be interesting to see what happens for example in these regressions trade measure which is embedding both intermediate and finals I would actually use some procedure to filter out what's intermediate and see what that horse race would would look like I agree with that that's a good idea thanks President Jen. Thank you well first of all it was a very very smashing exposition and I have to say that I see now things for a different angle it's very innovative if I may and I have to also have to say the dialogue was very very stimulating I have a question on the chart seven of your paper I'm not surprised by chart seven it goes in my opinion very much in line with the academia and even the IMF worked on that so I'm not surprised to see that we are substantially more open than the United States of America more or less at par with China it doesn't surprise me I think that also work has been done on the integration to the global value chain and we are more integrated in the global value chain than the U.S. so it's both I would say trade and the integration of global value chain where I am a little bit struck is that when you say we compare the average export intensity of the new countries with the rest of the union so it seems that it is an intra-average integration and with China and the U.S. if I understand well vis-à-vis the rest of the world so of course the question mark is what would happen if you would divide China in various regions or the U.S. in various states would you have more or less the same or not I open the question of course I would like very much to understand why you consider fully legitimate to make this comparison which seen from in the global perspective looks a little bit asymmetric. Thank you I agree the comparison is somewhat diagonal I again did with what I had I agree with you it would be very nice to have something inter-regional interstate in the U.S. provinces in China that these data just don't exist we don't have any regional dimension to the input output table of the U.S. for example that's what we would need to do that so I tried my best in the spirit of going somewhat less diagonal comparison that's why I included E.U. not E.M.U. so these are countries that are not in the E.M.U. but in the E.U. and that includes all the trade between these countries and within these countries and between these countries and the E.M.U. so it goes one step in that direction and you do find a difference which is smaller I mean that there's what I'm saying is that you know the U.K. and France are less connected on average than France and Germany so it is diagonal in that sense it's quite obvious but it is ensuring if there's a total difference what is the reality we have time for two more questions there thank you Jean for a very nice paper just on the big picture of the message however I have a I'm very much puzzled by your chart number eight in which you look at the bilateral correlation and how your measure of convergence you know the distribution was shifted to the right indicating much more convergence in GDP growth looking at the bilateral now this is the period you have two period the second period where convergence has increased is 2008 and 2014 which goes you know against the intuition I think of most people in this room that during the period of the crisis there was a lot of heterogeneity so I really wonder whether the cut off year which is 2008 is really the problem because in 2008 in the year of recession all countries moved together strongly because there is a big change in the mean which is negative but you know this distorts incredibly the picture so are we saying really that we have performance have been more similar during 2008 and 2014 that during the normal times this case perhaps you know you can start I mean that's that's exactly the regression I showed so when you do their exercise right I mean and you account for the crisis crisis is a bigger effect than the export intensity in terms of increased synchronization because everybody goes down and then when you interact that crisis dummy with each of the three measures export intensity trade intensity and financial integration you see that you know during the crisis financial integration increases and synchronization but their variable export intensity actually diverges more probably because like you know trade goes down and that affects the value change you know exactly exactly exactly yeah. Yeah I just first of all tremendous empirical effort and really imaginative and interesting but let me focus on the financial integration point and there I'm not convinced before the talk John and I'm partly not convinced because of the measure itself much as I have respect for Shevnam and my colleague Elias Papayano nevertheless I'm not sure that although the appeal of this measure is of course it's a parent exogeneity on the other hand it doesn't match at all with the data on financial integration the ECB publishes a every year a piece on financial integration in Europe and for several years they've been publishing these graphs that are about one price based and one quantity based measure of financial integration those measures show substantial increase in financial integration up to the crisis and then a very precipitous drop that levels out in 2013 if I remember rightly and then we're still not even at the level at the pre-crisis level so in a sense the output measure if you like of financial integration is telling you something very different from the input measure if you take the FSAP story as an input measure and I'd be interested in your comments on that. I think this is a question for Shevnam. Can I just show you one can I have my slides back okay I didn't have time to show this figure but Richard is exact right so obviously you have a de jure measure on the laws and you have a de facto measure in terms of movement of cross border assets and liabilities especially banking assets and liabilities so let me show you this figure this is from a forthcoming paper at IMF economic review but it is the exact estimate of the authors just plot it so they give you the aggregate estimation here what you see here the red line is the consumption smoothing right and then the green line is the income smoothing and the black line the dotted line is the unsmooted so you see how unstable the consumption because of the effect of the shock and because of the exactly what Richard just said the effect of the movement in the cross border assets and liabilities banking assets and liabilities but look at and that's you know the consumption smoothing is like if you look at the part before the asset and then it goes down drastically you know during the crisis and the unsmooted goes up the black line and then consumption smoothing come back up again and unsmooted declines but the point here is look at the income smoothing and how stable it is the green line so that is you know telling you right before the crisis EMU achieved around 20% risk sharing through the capital markers okay this is ex ante smoothing this is how much you smoot based on the cross border asset banking assets and liabilities fiscal assertion and all that on the other hand the income one that how much you know the effect of GDP on the GNI of the country smoothed ex ante is very stable and that's around you know 20% which is an amazing success for a union of you know 10 years when you know we are entering the crisis and it doesn't you know it goes down but not as much as the ex ante is very stable and that's around you know 20% which is an amazing success for a union of you know 20% as the crisis so I think this is also very important to understand because I don't think US achieved that amount of smoothing in the first 20 years of the capital market and banking union in the US so that green number for comparison person is 38% in the US thank you very much I have a final question for both you know what are your main conclusions and your main take a voice in terms of policy recommendations right so I think I think what this this paper says in this this measure suggests is that observed trade is just potentially the tip of the iceberg so there's there's there's much more to economic integration deep integration than just what is observed in what we've been using long for a long time just you know exports and imports between countries or between sectors that that measure is in perfectly measured it's it's got issues of whether it includes value added or other things and it's a bit of of action because it's virtually eliminating anything that is effectively non-trading services the most prominent on that list just by way of illustration in the data that we used in these 12 core immunocontries the correlation between services understood broadly on average between immunocontries is 0.6 it's quite large correlation and for most of these pairs of services we simply do not have all trade data and so perhaps because we should have these data and we don't measure it or perhaps because they effectively don't trade but they trade indirectly I think that measure has some something to say about that and I fully agree I think this this measure that really shows us deep integration and it tells us the importance of value chain and it's importance of so I value a lot this measure but going back to just we just talk about and Richard's comment we can think the same thing for deep financial integration right so you know EMU done tremendous job in terms of integrating financial markets but there is more to do and we can think that also in terms of how deeper we can go you know it not just single currency not just passing the laws but like you know how much more we can do in terms of the you know full-fledged banking union full-fledged capital markets union I mean that's just going to add more to you know GDP and consumption convergence which is at the end is very important from a fair point of view okay thank you very much with this very good recommendation I think that we have reached the end of this session thank you very