 Thank you Wider and thank you Carlos for organizing this session the paper that I'm presenting is a joint work with Viviana Arancilota and Joaquin Torres and it's also an empirical paper and what we aim here is to provide empirical evidence about the combined evolution of income inequality and the productive structure of the economies at the world level and we approximate the two dimensions of the productive structure the level of informality and the level of economic complexity. In the in the paper we explore iteration it is in terms of income level and regions and also different measures of income inequality I'm just presenting some basic results because this is work in progress. So this is a main question in development economics how are the levels and changes of income inequality linked to the productive structure of different economies. We know that they are linked mainly through the different intensity in the use of productive factors in the sectors of the economy and this intensity is reflected by the use of a skill or a skill labor force but also by the use of informal or formal labor or by the intensity in the use of technologies. So the literature analyzing income inequality has mainly focused on micro determinants at the individual level and mainly exploring the links between the role of the relative wages between skill and skill workers or skill bias technology and trade liberalization and to a lesser extent the role of institutional factors like wage bargaining or minimum wage. So here we try to to make this association with the with the production structure and we have like two attempts to do this as I said with informality and with the economic complexity of the of the production. In terms of informality even if these are two main feeders of developing economies the levels of inequality and informality there are not many studies that try to explore the link. There are some studies for different countries but not like generalizations. We have studies for transition economies, the studies from Rosar that find like a positive correlation between informality and the genetic coefficient. In the recent literature for trying to understand the decline in income inequality in Latin America there are also country studies that explore these links for Argentina, Brazil, Uruguay, Colombia and for many countries for some countries but this most of this literature which is based on micro data suffers from endogenous correlations. About economic complexity conceptually the idea here the idea is to reflect the diversity of individual knowledge in an economy and how this is combined and translated into the production and into what the economy produces and based on this idea there is an index that measures economic complexity by Edelman Hausmann and this index has been used in previous studies that approximate a similar question. We have this study for Hadman and Kossos that test the relationship between economic complexity I will later explain the intuition behind the measure and inequality and income inequality with a panel data for countries the usage in index from another world for another database that includes a lot of countries and from from them all we have some more recent papers that again explore this relationship for example this one from Lea Ambu who also find the idea that there is a negative association so when economies increase their complexity in terms of production there is a decline in inequality they also find that but when they do dynamic panel estimations the results change and there also some other papers that tend to find this same kind of association but in these cases mediated by human capital and literature review of this literature is there in that paper of Hadman. So what we do here we have a panel of countries for the period of 1990 to 2018 data for income inequality at the country level comes from the weed database. We tried with two measures of informality one is informality as measured by the by ILOs the share of informal employment. The problem there is that the coverage of this database is not good enough so we the sample is restricted and then we use the share of informal output in total output for the informal economy database from the World Bank and some are estimations on the others are modellizations of this measure so it's a new measure that tries to reflect the percentage of informality the percentage of informal GDP and the index that I was mentioning the economic complexity index combines information about the diversity of the production of a country so the number of products that a country exports and the equity of these products that is the number of countries that produce an export the same type of the product so the idea that more sophisticated economies are diverse and are able to export products that on average are only produced by a few number of countries. The method is similar to the to the other papers we also have a country fix effect and not there I don't know why but it's basically trying to to estimate the link between inequality and the measures of the productive structure these two measures and we have control variables and we also do estimations for different regions and groups of our countries but I'm here I'm presenting just some basic results first is something about the data that we all know but there you have the the gene index for this from this database and we can see a higher inequality levels for South Saharan Africa the green and also for Latin American countries the yellow ones about informality we have as I said we have two measures but this measure of ILO is very incomplete so we can we we don't get results with that but the other measure about the informal output as percent percentage of GDP again we have a higher levels of informality for Latin American countries and from Saharan countries and here we have this economic complexity index the I should have said this before but the colors reflect the region so again we have South Saharan and the green and Latin American the the yellow countries with low levels of complexity and we have European countries at the top these are the changes we have here just to to give an idea we have three points in time well of this economic complexity index and what we see there is that the the blue dots that show like the the trend in time increased for East Asia and the Pacific and for South Sasha that those countries were able to to make their economies more complex in terms of the production and rather a quite stability for example for Latin America or South Saharan Africa so some descriptive statistics the association the positive correlation that I was mentioning between GDP and informality we can see it at the at the world level also both with informality in GDP it is the upper part and also with employment but it's not so clear and then we have in some the same in general terms in all regions but with some exceptions and we haven't this correlation between Gini and the economic complexity index so this is like the first chess correlations and what we have there in the in regions is that that negative correlation is not you cannot see that for the yellow dots that are the low income countries that is different a different correlation there so some basic results I will present the results without without the set of controls because they are they when we are proving the contrast that the main result remains so I just present the basics to give the idea of what we get we get in the first column the positive association between informality and measure with the GDP measure and inequality and that that estimation only includes a counter for GDP in the second case with with employment we we do not with the I law measure we do not get any results and with economic complexity complexity we get in the third column a positive result which is different from previous results in the literature and that what we are now trying to to understand and when we in the other two two columns we we estimate to we using the two measures and the two measures of product extractor and the results are maintained and what we do here trying to understand this difference with the previous literature is that we consider economic complexity and also in a nonlinear way in the first column is just a repetition of what was I showing the positive relation but in the second one what we do is that we include the quadratic term and in that case we have the a positive relation and then up to a certain point of a certain level of complexity of the economy and then a negative relationship that it's similar to what previous studies found if we or what is the difference with that one if we include the control with GDP that remains and if we split our sample between high income countries and not high income countries we have a negative the negative relationship for high income countries and the positive for not high income countries so what we are now exploring is like trying to we we we took the the previous evidence from article and replicated it it's not exactly as the second and third columns are the replications we get results very similar to them and then we change only the sheenish as to see if the differences in the world database are are driving the results and it's not the case with the results we get that so what we are now exploring this is full ols because they have this and they have also fixed effects but we cannot reproduce them because they add some data that it's not available but we are looking for that but basically the the the user an aggregation for for for for periods of time uh averages for two periods of time so we are like exploring if it's the fixed effects the aggregation or or just the fact that the linear that the contrast that they are in but when we do that we sometimes it's the same are like a driving those we start but basically what what we have up to now is that the levels of informality measures a percentage of cdp are associated with higher levels of income inequality and contrary to previous evidence economic complexity is associated with higher levels of income inequality but when you do a non-linear modernization that depends on the on the level so as economists get more complex they present lower inequality but after a certain level of economic complexity so that's it okay thank you