 I'm going to, despite the title of the session, I'm going to be talking about within country inequality almost exclusively, and that's obviously constitutes the building blocks, one of the important building blocks of global inequality, which I'll come back to. This is what I'm going to do. I'm going to tell you a little bit about LIS, which is both an institution and a database. Many of you, I think, are familiar with it. And then I want to give four quick empirical snapshots. I might toss one depending on the time. One, I'm going to talk about income inequality in high income countries. I want to say something about the middle of the distribution, the famous hollowing out of the middle. Then I'm going to talk about what we've learned by adding middle income countries to what has historically been a collection of data sets from high income countries, something about wealth. And I was asked to talk a little bit about some of the analytical challenges that we face at LIS, but I think I'm probably going to leave that for the discussion and for talk throughout the next couple of days. Okay, very briefly, I think many of you know what LIS is, but I do want to linger for a moment to tell any of you who don't. LIS, which is located in Luxembourg, is now called LIS, the Cross-National Data Center in Luxembourg. We've been in existence since 1983. I've been working with LIS since 1989. And we are a provider of cross-national over time microdata by microdata, I mean records available at the level of households and persons. We currently include microdata from about 50 countries, mostly high and upper middle income countries, and those data are organized into 10 repeated cross sections at intervals used to be five years, four years, and now three, from 1980 to 2016. I'll pause to say that some of what I'm going to show you in terms of change over time looks fairly different because the time period here is rather different than, for example, what Tony was just showing. Our claim to fame, I would say our value added at LIS is really two things. One is extremely, we do the very labor-intensive work of data harmonization. We don't field any surveys, so it's all ex-post harmonization of surveys. The databases that we get are based on household surveys. Some have administrative data in portions of those, and we recode all of it into a common template variable by variable. The second is we make the microdata available through a remote execution system. I can say more about that later for anyone who's interested. So something like 7,000 researchers have used our data over time from universities, government agencies, NGOs, and so forth. The primary focus is income data. We also have a lot of microdata in various data sets. It varies, but labor market data, consumption data, and increasingly, we have wealth data. It's a venue for teaching and virtual community for methodological teaching and thinking. We have a long history of collaborating with a lot of supernational organizations, the IMF, the ILO, the OECD, many UN organizations, and I'm happy to say, of course, I'm lingering on that. LIS has long been one of the data sources for wider, well-known, WID database. Okay, a couple of, as I said, I'm gonna give you some very quick empirical findings, and my purpose is two-fold to show some substantive results, but also to give you a sense. I'm gonna go through them quite quickly to give you a sense of a sort of the power of the data, what we can do with household survey data over three decades. So the first question is, it's been widely reported, although not in the last half an hour, that income inequality has increased in about two-thirds of high-income countries. Is that accurate? And what do we mean by that? Is it the pre-tax and transfer income, what we call market income, or is it the post-tax and transfer? I'll say more about that in a moment. What we at LIS call disposable income, or is it both? So I'm gonna show three quick slides, 14 high-income countries. I chose these out of the 50 because we have both very detailed pre- and post-tax and transfer data, and also we have a 30-year sweep of time when these data sets. So what you're looking at here then, fairly simply, most of you are familiar with almost all of these indicators. These are these 14 high-income countries. The data are from 2013, our last completed wave, and these are households with non-elderly heads. The story of inequality and redistribution looks very different, obviously, when if you do or don't include pensioners. The pink bar, the longer one, these are all genies, which are known to all of you. The pink bar is the genie of market income at the household level, where market income is the sum of income from labor and capital, and the blue bar, what we call disposable income, I put DHI there for those of you who are less geeks, that's the name of the variable, and that would be market income plus public and private transfers into the house, mostly public, and then minus direct taxes paid, that social contributions paid by the household and income taxes. Okay, so that's the story there. So what do we see? We see a certain amount of homogeneity, obviously, the post-tax and transfer inequality is always less, but we also see a lot of cross-national variation, and I think the story there is a fairly clear one that national institutions matter, and they matter a lot, among these otherwise somewhat similar countries. Market income inequality varies quite a bit from a high of 0.53 in Ireland to a low of 0.38 in Norway, not too surprising. In general, the high market income inequality countries are the Anglophone countries in Israel, and the low, of course, the usual suspects, the three Nordic countries here, Finland, Norway, Denmark, and also the Netherlands. Disposable income is a somewhat similar story. The highest inequality in the United States, that's a well-known finding. If you add many more high-income countries, you'll still find that, and then tied with Israel, and again the lowest in the Nordics, and in the Netherlands. Now very common exercise at least, most of you probably know this, people at the EU do this in the OECD, we look at the difference between the length of these bars as a proxy for redistribution. Obviously, to some extent it's a crude measure and it's a fictive. The market distribution, of course, is fictional because if the taxes and transfers were removed, we know we would have behavioral and demographic effects, but this is simply an accounting exercise. I'm dwelling on that because I wanna show this picture, which is a recasting of the exact same numbers, and just to highlight the policy story, and I think one that's very active, especially now, on the horizontal axis, these exact same numbers you just saw, that's the market factor, but it's the same variable, the market, the genie of market income at the household level, and on the vertical axis, it's the redistribution, it's the distance between those two bars. So just to remind people that the way that countries can move to lower levels of disposable income is either by shifting left, right, narrowing the market income distribution or shifting upward. So just to start, I think an interesting example, comparing Germany and Ireland, both with disposable income, disposable income inequality, exactly the same in our data 0.29, where the market income inequality starts in Ireland at 53 and in Germany at 41, so Ireland, the redistribution level is literally twice as high as Germany with the same outcome. So it's a similar discussion we are often having in the U.S. as marked here, U.S. income inequality very high could be reduced by moving the U.S. to the left. We understand certain set of policy levers strengthening unionization, raising the minimum wage. Those are both rock low in the U.S., rock bottom low, or moving upward by more generous and more redistributive taxes and transfers. So I think the policy story comes out very nicely that one way that people have used the list data forever are these pre and post comparisons. Now very quickly, this is showing change the time that many of us are looking at at least 1985 to 2013 and some data sets 1980 and actually we do see more rising inequality. It's of course beware your end points. I think Tony, your story, I'm also showing here the U.S. and Finland and in fact from 85 to 2013 we're finding a two to six point gene increase in both market and disposable in both the U.S. and Finland. So that flat line that you're seeing a lot of this change was happening before 2000 but these measures or these stories of whether income inequality within countries are going up or down extremely sensitive to the beginning and end points to the income definition and also obviously to the metric. Okay, the hollowing out of the middle. This is an interesting question that we're hearing about a lot in upper income countries. We're hearing about the shrinking middle while in many middle income countries we're hearing about the growing middle. What does it mean? At least many people for a very long time were studying inequality and poverty, both absolute and relative poverty and then obviously in recent years there's been an enormous literature on the top. We know that income and wealth literature has grown enormously especially with the use of tax data in the last decade. Some work that I have done, as you mentioned two of my close colleagues from LIS are here, Marcus Yanti who I worked with for many years and Daniel Kekke are both here. So Marcus and I edited a book with 17 studies from LIS and we focused on the middle thinking that it had been somewhat neglected and I think what we learned was first of all there's no universal definition of the middle. It's measured many different ways. We thought it would be clever to allow 17 sets of authors to define the middle as they wished and they came back with 24 different definitions. So that was, and those were all except for one based on where in the income distribution the household set. So what I want to just note here is the question about what's happening in the middle needs much more nuance than it's I think received to date. So let me see if I can persuade you of that quite quickly. These are those same 14 countries. This is a rather expansive definition of the middle. Setting the definition of the household is middle class if their household adjusted size, household size adjusted disposable income falls between half and twice the within country median. So below the middle class then would be a very common poverty measure which is the 50% of the median that's commonly used at OECD and lists. I think many of you know Eurostat tends to use 60. So what are we asking here in that same period of time that we were studying what percentage of households were in the middle and how did that change between these two time points? And what you can see here is the, look at Ireland, the percentage of households that were middle class rose by five points on a base of about 70 in Denmark virtually no change and all the other countries, the middle fell. In other words, the percentage of households that fell between half and twice the median and of course these are comparing cross sections. There's not a panel data linking households to themselves. These are different samples. So what do we see? It's obviously a pretty clear pattern in terms of direction of fair amount of homogeneity in terms of the magnitude from mostly in the single digits, the upper single digits in Luxembourg, 13 percentage point decrease in the size of the middle class in Israel. But I think the more interesting question and one that has a lot of both economic and political implications is where did they go? So in this picture, which is a little bit ugly but that answers the question in a very simple sense that the bars to the right tell you which these reflect increases. So in other words, if the bar to the right is pink that reflects movement into the top. In other words, upward out of the middle class and the blue would be movement into the bottom and then on the left are the few cases where there was movement into the middle class. So what you see is a lot of different things are happening here. This Israeli case with a 13.5% drop, percentage point drop in the size of the middle about of the 13, eight went up and almost five went down. In the Irish case where the middle went up by five, three came into the middle from the top and two came into the middle from the bottom and et cetera. So there's all kinds of variation in these numbers and it's in fact often asked, I think in these many conversations about absolute versus relative wellbeing. If the middle class is shrinking because everybody's going up, should we be concerned about that? Well obviously political scientists are concerned about it for all kinds of reasons and if we believe in the price of inequality and many of those questions that we care of what's happening in the middle class, the size of the middle class, regardless of whether they're all going up or mostly going down. The other important thing to know very quickly, this is not a very beautiful picture. It looked much nicer when it was in the New York Times and was interactive with something from the upshot. Basically the red line is showing the US, the other 13 countries are in gray and that's simply showing the real income levels at the 10th, 25th, median, 75th and 90th. The point of all that is if you look at the US case, what you see here is while the size of the middle fell, it's absolute wellbeing went up. So when we hear all about the collapse of the middle, we really have to ask carefully, what do we mean by that? Okay, what happens when we add middle income countries into this story? So lists for many, many years, for a variety of reasons, the data that we had access to, the intellectual community that we were part of, et cetera, we were almost entirely in the world of high income countries until 15 years ago when we made a very concerted effort to add middle income countries, which caused us to pause for almost two years and redesign our data template. We had to take much more account of informal labor, transnational remittances, consumption from home production and so forth. We then have added something like 20 to 25 middle income countries, it's a little tricky because they move a lot. So at one point while we added, the number actually went down because most of Eastern Europe moved from upper middle into high. Nevertheless, that's been our emphasis and our growth emphasis and it will be for the next many years is why we're up to now about 50 countries. So what do we see here basically? This won't surprise many of you, but it's important to note, this is exactly the first picture that I began with, but now I added some more high income countries and some middle income countries and of course they're quite starkly piled at the top, the US and Israel exactly where they were before. Disposable income genie at three seven and above them, Guatemala, Peru, Brazil, Panama and South Africa of course at the highest and those are the highest in both disposable and market. Panama has now become high income but was not at the time of this wave. So we know it's a story that's well known to many of you least at this period of time from the early to middle ladies until the post recession period while income inequality was in fact rising. According to our data, OECD finds the same in two thirds of high income countries. Of course it was famously falling in Latin America in the same years I think norelistics work showed also with household survey data in 17 of 18 countries. Latin America fell at the same time that it was rising in the high income countries but they still remain after the period of decline more unequal. And this is simply recasting the middle class picture that I talked about a moment ago. So what you see this is again a rather expansive definition of the middle class from 50 to 200 but this picture would look fairly similar at many of the other common definitions 75 to 150 et cetera. So you see again here that the middle class defined this way and it's one way of course of many many and just the income distribution and so forth it's a sort of economic definition of the middle class not a sociological one. We see again that the middle income countries have the smallest middle class still substantial by this measure about 40 to 60% of households but less than say the 70 to 80 in the rich countries and then we see the high relative poverty rate. The purple on the left, okay. This is my one foray into sort of a link into the work of many of our colleagues here today on global inequality and my colleague Bronco Milanovic who's now works with me at the city university of Newark and obviously is they well connected to the elephant graph and much of this. It just to remind us that of course I've been talking about within country inequality but the list data are very useful and have been used in the building blocks of a lot of Bronco's work on global inequality. So again just a reminder about between country variation so the 10th percentile in Switzerland is about the same as the 90th in South Africa and twice that of the 90th in Guatemala. So obviously and this is of course price adjusted and how a lot of people I think you all know that make the global estimates is by aggregating our micro data and then with many other sources as well. I have my eye on the time. I'm just gonna close then very quickly. Again this ties in with what you've seen in the last two presentations I think especially Tony's. What do we know about wealth? So we've also while we were moving into middle income countries on the income side we also did a lot of work in recent years to bring in wealth data sets. Most of our income data sets did not have wealth data. The wealth data sets almost all have income data. They're mostly modules on income surveys. So that allows us to look at the joint distribution of income and wealth which I'm gonna show you very quickly. So I have 20 seconds left so let's see if I can do this. This is very similar to what you just saw at the global level. Six countries where we have wealth data from wealth surveys and this is wealth, here I'm using net worth which is financial and non-financial wealth net of debt. And then you simply see again with a genie that the wealth genies are about twice the income genie. So Finland is the low on the income side at 0.26 up to the US at 0.47 and on the wealth side the whole thing is about doubled. Slightly different countries, Italy coming in at the least and of course the US famously last and the shares tell an even more stark story. And finally this, one of many ways to depict the joint distribution of income and wealth. Again, this is from the same surveys and this is a heat map. So what you see here, there's six countries if you can read them Australia, Canada, Finland and then Italy, the UK and the US and on the horizontal within each country it's the five income quintiles with the cut points based on the income distribution and on the vertical are the five wealth quintiles likewise with wealth, of course, cut on the wealth distribution and then the 25 numbers add up to 100. And here you see of course the darkness in the 45 degree line from Southwest to Northeast is showing the correlation. There certainly is a correlation, it's by no means perfect. So the way you read this is in the US which has the strongest correlation among these countries in that upper right dark square about 12% of American households with these adjustments that we've made are in the highest income quintile and the highest wealth quintile. That's the highest number among these six countries and then likewise about 6% of households are in the bottom of both income and wealth. So the nice or helpful, many, many people are looking wealth as you've been hearing in the OECD and the Credit Suisse and so forth in us is that we're able to see a much, much richer picture of household economic wellbeing when we can look at income and wealth together. Thank you. Thank you. Thank you.