 Thank you very much. I'll be presenting our work on the dynamics of multidimensional chronic poverty that we did with Rocío Garcia Diaz from Tech of Monterey in Mexico. Just as a motivation, it has been vastly mentioned in the literature about the importance to distinguish between transient and chronic poor because as we know, public policies needed to tackle each one of them can be different. Now more specifically for the chronic poor, the longer a person spends in poverty, the harder it is for that person to go out from poverty. So assistance and the greater the need for assistance and so we know that chronic poverty is important. Now among since the theoretical work of San and since the proposal of Ocurian Foster, among practitioners there has been a tendency to approach poverty as a multidimensional phenomenon and although this trend is also true for chronic poverty, to tackle chronic poverty as a multidimensional phenomenon is in its earliest stages. So that's one of the things that we want to do here. So talking a little bit about the literature and how to include time on poverty, broadly if you separate the measures I think first if we consider the intertemporal poverty measures, these measures are sensible to the experience of poverty of the individual but as a measure in itself they don't formally distinguish between the transition poor and the chronic poor. And we want to focus just on chronic poor. So in the second category I think we can mention just chronic poverty. That's the one we will focus on. And within this category you can further divide them I think in permanent income approach and the spelt approach or counting approach and we'll be using the later. Okay. Now in this paper what we're doing is we are taking one of the multidimensional chronic poverty measure proposed just recently by Alquire, Chacabarty, Pavla, San Janolet and Lonecki. We're changing it a little bit you will see. And what we're doing is we're applying sharply decomposition to it. Now well the compositions are vastly used to disentangle the driving forces of change in poverty and there are many decompositions in the literature but broadly sometimes the marginal impact of each factor of the composition sometimes it's not intuitively. It's not intuitively. Sorry. And since there are some other problems like path dependence and I think in the sharply decomposition deals with some of these problems that's why we're taking it. It also adds the advantage that is exact additive. So before we go to the decomposition I'll check I'll just revise the index. So if we see the multidimensional index as a product of the head of the headcount sensor headcount and the here and the matrix of intensity of poverty what we have in this index is that in the headcount we have a dual cutoff of dimensions and of tau so a person is considered chronically multidimensional poor if it's poor in more than k dimensions more than tau times so you need a pane of data. And what we have here in the matrix A it's it represents the average deprivation share among the chronic poor through time. Okay so just to so when we apply the sharply decomposition what we're really doing is we're computing the marginal impact of each of the factors and as they are eliminated in succession and then average in these marginal effects over all the possible elimination sequences. And when we in one of the applications of this decomposition as proposed by Schrocks is that we can divide we can have the exact additive of the between group effect and the within group effect by subgroups. So that's what we have here and the between group effect and the within group effect. So now in the within in the change in M we know that by construction this index it can be decomposed by subgroups so that's something that we will take in consideration. Bear in mind that and then when when we have the multidimensional as we mentioned it is the product H and A and when we apply the sharply decomposition we have we can decompose it to see what portion of the change in M is due to incidence and what portion it's due to intensity. And if we go back to the to the to the the previous formula and we just substitute by subgroups we we can have this broad decomposition of the demographic effect and the within group effect we can divide it in incidence and intensity. This work for the cross section was done by Roche and we're expanding it to to chronic to a chronic framework. Okay so we applied this to a penal data in Argentina. This penal data has a format of 2-2-2 which is they they sample the the individuals two quarters they they don't sample them for two and then they sample the following two so we have a panel of three of four observations during a year and a half just some and we will manage groups household groups depending on the presence of children and of elderly in the household so we'll have with it yeah we'll have those four for groups and broadly with the 2004 the of the total multi-dimensional chronic poverty the household two which is the house was with only children where they the most deprived but by 2012 they they improved a lot and in household three which is which are the households that only have elder elderly population perform the worst okay so just revising some some general know that the index and in itself in the proposition of Korea when when they propose a multi-dimensional chronic poverty they don't do it with data from Chile and and they manage and they use these dimensions so for the case of Argentina similar country we use the same and same cut-offs because just the choice of dimensions and cut-off is a big big task so that's where we did notice one of the problems that we had with income well we suspect that there was a under estimation of inflation in Argentina but we had a hard time finding an official measure of inflation but we just we someone mentioned to us that somewhat international organizations are starting to use another a private inflation index and so we will do the exercises again with with this inflation measure but just to let you know we we did this all exercise with and without income and although the coefficients of the results change the order the relative importance of the dimensions and groups don't change okay so this again general results at least 63% of the population had incidents in poverty in at least one dimension through but in the fourth period and we'll take the cut-off of three and as you can see when we are talking about chronic poverty there the the percentages are very very low so we'll we'll be decomposing an already low percentage of population so that's something to bear in mind okay so when we apply the decomposition these are the results so again the group the household group that performed the most the best was the household group two and the worst the household group three okay to see which one which group draw drove the change as it's clear group household two with responsible for 77% of the total change and household three just for the four four four percent and of this change the within group effect was the most important it's almost all of the effect was to to the within group effect as you can see 97% and with further decompose it we see that the the change was driven by incidents and not not so much of the intensity and further more by construction when you see the decomposition of a when you when you want to see the most important variables it's well it's not shocking that income is driving the change but and although the overall picture is very optimistic as you can see it is not so much for the for the for when you when you see the details okay there was something else that we did as we we have a rotating panel data so you have it interposed panels so you what you can do is you can through time you can you can follow the you can do the the sharply decomposition multiple times over time so this is this is comparing panel one with panel two this is 2003 2004 and so forth so this is how the sharply decomposition behave through time how the driving forces are changing through time and when we do that we see that there is a there is a pattern we identify a pattern of the households and we see that the household too was driving the change all the time but when we try to identify a pattern of the indicators it's much much harder to to identify a pattern so one of the I think possible implications of this is is that to use the household groups it's more informative at least for the case of Argentina and chronic poverty in Argentina to manage household groups is more informative than then analyzing indicators so these are the conclusions I think I already I said them through the through the presentation and yep that would be it thank you thanks very much