 In my case, I'm going to give you an introduction, a brief introduction to the World Income Inequality Database that we hold here at WIDER. I thought that in a conference that is showcasing the research topics in which WIDER was involved or engaged during the last 10 years, would not be complete without a reference to one of the databases or one of the tools we have in WIDER that has been lasting for a longer time. So maybe some people are familiar already with this database and maybe some people are not. So I want to emphasize a few characteristics and the purpose of this database. In general, in WIDER, a part of producing research, we are disseminating the results of that research. We are also interested in contributing to the research community and policymakers. And in general, people interested in development economics with data sets or databases that could be very useful and will be accessible. There are some databases on social assistance, on government revenue, and this is about inequality across countries. So as you know, there is a big concern, a big increase in concern on income inequality all over the world. This occurs at the global level. Like we've seen in the previous presentations, what is going on within equality in the world is increasing or decreasing. At regional level, so interesting in what's happening in Latin American countries in Saharan Africa and in Asia or at the country level to know the different patterns in terms of inequality. But this always faces the lack of adequate data. So the big problem in analyzing, especially when it comes to global inequality or cross-country comparisons, the main problem we always face is the problem with data limitations. So behind any graph that you see about global inequality, for example, there is a lot of work done to compile information for many countries that usually, very often, is not fully comparable to imputing what happens with countries for which there are no information, et cetera. This is especially important for developing countries. So in economic development, we are interested in low income countries, and this is where the lack of data will be more important, especially in many countries or in Saharan Africa. Also when it comes to long-term series, so because of the lack of data or the lack of comparability due to changes in methodologies and sampling methods, et cetera, in what surveys are used to compute inequality. And also when we want to make comparisons across countries, some countries use income as the main well-being indicators. Some countries, they tend to use consumption. And even when we talk about income, there are many different ways of defining income, and they are not comparable always. So limited information disperse across many sources. If one wants to know inequality in all countries in the world has to consult with many sources. And not always easily accessible, and as I mentioned, with severe comparability issues. So in 1981, the Indian Square compiled a database for the World Bank. And some assertion that is in the paper, I think it's still quite relevant now. So this idea of the empirical work using cross-country data, the lack of time is serious, especially for the hot topic at that time. That was the relationship between economic growth and inequality. Faced this problem of lack of data, problems with the quality of data several, many times. That makes comparability between countries very difficult, or even in the same country if we want to analyze over time. I think this many years after this paper was published is still unfortunately true. Even now, the range of topics for which we want this data is probably much wider than it was at that time. So following this denigrant and square compilation of inequality, genies, or population, income, or consumption share for many countries, it was constructed this database, World Income Inequality Database, WEED, and that was held here at wider. So this is a database with basically cross-country information for income inequality. It's a story in an organized and user-friendly manner. So it's basically an Excel file or a data status file. And that is quite accessible so that you can just download from the web or using some of the graphic tools available in the website. It is part of the current World Inequality project in our wider program that is finishing this year. But it has been an essential element in many different world programs. So it was compiled initially in 1997 and 1999 with Andrea Cornia being the director of wider. But it survived the different world programs. It survived Tony Shorrock's terms, Finn Tarf's terms, and hopefully it will survive the next term with Kunal Sen. The data was updated several times. Now the version that is in the website is 3.4, that was compiled in January last year. And we are currently working on a major revision of this database and probably with Sun Enhanced Future. As Tony Shorrock mentioned, this is a collaboration we have with Tony Shorrock's and Chinar Baimool. And we hope that in the next future we will be able to produce this revised version and with these new features. So basically, we report information from G&E for different countries, the style, quintiles, the bottom and top 5% median income, and some description of these variables. So what is the source, the concept that they are measuring, is income, is consumption, is per capita, equalized, et cetera. The coverage is for the whole population. It's only for rural areas, for urban, for part of the country, et cetera. This is a secondary data, so there is no micro data. So that cannot be frustrating for people like me that are used to put our hands on the data. So this is basically genes that have been reported by other sources previously. So the data contains all the new data sets, so these initial compilations by the World Bank, but also compilations by CLAC, by UNICEF, Transmoney, or by the Asian Development Bank, and others. We complement this data with other sources, like Luxembourg Income Study or SEDLAC, that both generously provide us with the last updates for the genes of the information they have. So that makes a good complement to other sources. We also take data from public sources, like Eurostat, World Bank, OECD, E-CLAC, et cetera. We actively search for information from national statistical offices, even though unfortunately information on inequality is not as standardized as information for CPI or GDP or other issues, so that makes this task difficult. And as you know, some information can be reported in many different languages, and even that we are a United Nations, we are not just strong in some of these languages. And we also look at the research papers, reports, et cetera, where we can have information. The WIT has played an important role in research over the last years. In terms of global inequality, as we have seen before, when you try to measure the level of inequality in the whole world or in big regions, like Sub-Saharan Africa or Latin America, so for example, the seminar paper from Sally Martin in the Quarterly Journal of Economics, so more recent research, for example done by univider researchers using the WIT recently, like paper by Nino, Sara Sua, and Tarp in the review of income and wealth or Rupert, others in economic letters. But it has also played an important role in other issues in which we are interested in the relationship between inequality and, for example, other issues related with the stability of a country, macro-stability, like foreign-dieted investment or real change rate or institutional development, labor regulations, the impact of economic sanctions on inequality, the expansion of public sector. Sometimes inequality is the dependent variable. Sometimes it's the variable that explains this other issue. So also social conflict, religiosity, skill immigration. So I think there is a big range of topics for which we need data on inequality and WIT has played an important role in helping to answer to those relevant questions. Of course, there is an increasing number nowadays of other databases all over the world. Some of them has been built largely using our WIT database. There are anti-harmonization, like probably your stat is the main example or the efforts of the World Bank in somehow standardizing some ways of how to capture information on income and consumption in household surveys in developing countries. There have been also some important developments in exposed harmonization, probably like similar income studies, the best example for middle income and high income countries or the set lag for Latin American countries. That is also in collaboration with the World Bank or the World Bank also collecting pop-carnet information about the distribution in many countries, OECD, et cetera. We take advantage of all these advances in the sense that we try to pick all this information and combine it with information we already have. And of course, there are other specialized databases like the Walling Inequality Lab dataset for top incomes or the credit use for wealth, et cetera. The fact that we have different sources of data to analyze inequality, that is also, I mean, makes important some critical assessment of the implications of using the different datasets. And there has been some literature also analyzing the flaws and the advantages of different datasets like papers like Adkinson and Brandolini in the Journal of Economic Literature or the special issue of the Journal of Economic Inequality edited by Chico Farada and Nora Lustig or another paper more recently by Galbraith. In this sense, the wheat, I think, like other sources, it has some advantages and some disadvantages. So we see the wheat not as a substitute of the other sources, but as a complement. So among the flaws, of course, there is some limited information. So we provide basically a few statistics. If you are interested in very specific statistics like the genie for gross income for non-enderly households, like we saw before, you need more flexible tools like Lease that allow you to define the sample and the concept in more detail. If you are interested in more standard concepts like genie for per capita income, disposable income, then you will find that in the wheat. Of course, there is a heterogeneity of data sources that makes sometimes rise problems with comparability and differences in the way the well-being concepts were defined or in the methodologies used to produce them, et cetera. This means that our data comes with a warning alert, so used with caution. Sometimes, depending on what is the purpose of the research or for what reason do you want the data, you might need to pick specific observations or to try to make some harmonization of your own. On the pros of our database is that it's quite accessible, it's transparent, you know what you have. It's, and especially it gives you the largest possible geographical and time coverage. So you will have the largest sets of countries and the longest periods of genie that can be computed from surveys or administrative data, et cetera. So that's somehow the main strength of the database, especially when it comes to developing countries. The challenges of this database for the future is trying to be faster probably in updating information, so that people don't need to wait one year or two years to have a new update. As now, information is produced in a continuous way by the different sources. We aim at also being able to update the database in a more continuous way. We are exploring with Professor Shorrocks and collaborators ways to improve some estimates that can be improved with information we have when there is some mis-engineers, but we have information for the distribution, for quantiles or the size, or when we know that the genie was computed based on group data and there are methodologies that maybe allow us to produce a genie that is closer to the real genie or maybe correcting for top incomes, et cetera. Also, we plan to try to help users with standardization of the data, so in the sense that they can have serious for, say per capita disposable income or for consumption for many countries and for long time periods with some adjustments through the data we have. And also to improve the information that is displayed in the website, the documentation, some visualization tools like maps or graphs that you can directly construct with the data we have. And also introduce some analysis of this data to help people interested in these topics. The new version that has not been released yet, but we are working to have an idea with about 11 observations. Of course, that means for one country in Gia we can have several genies. We try to avoid duplicates in the sense that we try to have information that we only have more observation for one country when they add something like maybe one is for consumption, another one is for disposable income, another one for gross income or urban areas, et cetera. And we try to have the largest possible coverage. In this version we will have at this moment 191 countries of historical entities. So we have some information, sometimes very limited, but some information for all countries except for Libya, North Korea or some Gulf states and some micro states and some special territories like with Sahara and French Guyana. But I mean we have some information for more countries all over the world. As you can see in the graph, most information comes from high or middle income countries but we'll make a special effort in having some information for low income countries. Even if it's more limited, it's also more valuable for us. Just to show the temporal coverage, we have observations from the 19th century but of course that is anecdotical. Most of the information starts in the 50s and 60s but somehow covers the period for which we could be more interested in analyzing global inequality long-term trends. We have some relevant number of observations for Saharan countries that, as I mentioned, jointly with MENA countries are the areas that for which is more difficult to find observations and we are also improving in this case for the MENA countries. So in general, I mean the main challenge of this kind of data set is that we're trying to find this lack of data putting on the table all the information we have but it comes with all this problem of difficulty and comparability of the different concepts, different sources, et cetera. And we will try to make the effort, I mean all the effort we can in trying to make things more friendly for users, especially for non-technical users that want to have information that can be used without previous harmonization, standardization, et cetera. But of course, there is this trade-off that somehow I think in terms of inequality measures, there is like a gap between the demand that is increasing very fast and the supply that cannot increase as fast as the demand because of course we cannot go back in time and make a service in countries for which we don't have information or we cannot force countries to produce information if they don't do it. So the supply is quite limited but I think we try to make the most of it so we can answer some of these relevant questions about inequality, the trends and the relationship between inequality and other relevant social phenomenon. So thank you very much.