 This is a topic that we are calculating as a complement to the model. We assume that the model is correctly designed. And we are using the model to further calculate vulnerability to poverty. We calculate poverty or negative poverty using disposable income. In the presentation I will talk about poverty and vulnerability, some concept of definition. Then I will present the tax inflation, the vulnerability estimation and some results. So, to start then, what idea are we doing this? We know that over time people might flow in and out of poverty. So, this is a manual. Sometimes people say this for people in the threshold is not good. Sometimes poor, sometimes not poor because of the way poverty is measured. The concept of vulnerability to poverty consider the probability of being affected by external shocks. And developing countries vulnerability is not considered. And seeing some kind of model to suggest for policy design. Then, what's the difference between poverty and vulnerability? In poverty we are using and we are measuring and this is an ex post-independence thesis. We know who is poor after the damage is done. Then the difference in vulnerability is ex-independence. We have to identify who are prone to be poor. So, this is a compliment. I would say it's a dynamic estimation of poverty. Then, how are we estimating vulnerability? Vulnerability is a probability of being poor in the future. So, we have here that a person is considered has the probability of being poor in the future. When the next period or the future consumption is less than equal to the poverty line. And the poverty line is given by the characteristics of the household. And the external shocks also. And where X is the household characteristics in age, sex, number of children and some other characteristics. Vega are the state of the economy. Alpha is the household 11th effect also where the household is current in the future. And the other thing is the idea of geographic factors that those are the shocks. It might be a serious sickness. Vulnerability is the probability of becoming poor. And that's the function of the expected consumption in the future. And the variability of the consumption. In this case, we are using as a proxy of consumption that is possible in the future. When there is enough data or in developing countries, especially in developed countries, there is no deployment data of sufficient length of time. I would say 10, 15, 20 years. But in developing countries, we don't have that luxury, I would say, to follow people through time. So we have only one observation at the time and the limitation to estimate the probability of becoming poor. So we are using a purely mathematically or econometric model to estimate vulnerability. Alpha is the methodology. We use a model of the model designed for Ecuador. And it uses a household national survey on income and expenditure. This was done in 2011. The survey was given to 150,000 individuals and that gives 39 or almost 40,000 households. That gives an average of 3.1 to 3.2 persons per household. What's the estimation? We estimate probability and vulnerability to probability by using a model and running the program from 2011 to 2016. And since the model is set for 2011 and besides the vulnerability, it has a couple of strong assumptions. We assume here in the Epo model so that there's no label market changes. And we estimate simply the tax benefit system. Based on that, on the Epo model we actually have the critical household head. This just to summarize that as usual, I would say worldwide women are the most disadvantaged sector of the society. For example, the median rates for female household heads are higher. They have less education, more. For example, women have 52% at most primary education. But we have a hundred percent. And again, social security women, no social security almost two-thirds of the household head women don't have social security. Then what are the results? We have that the tax policy benefits are designed for the purpose of distributing the income. For example, the share of taxes almost the totality are paid for the richest side, like 90%. At the same time, social security or social insurance contribution is proportionally paid by the richest population. At the same time, the benefits or the distribution of the income is given in half percent to the poorest sector of the population. So this just shows how the tax policy in NEPRO is doing what it should be doing. So these are the results of the NEPRO model. Using these results, we use to calculate the women are being results. And then we have here, given that we are calculating poor and not poor population and vulnerable and not vulnerable population, we have these four groups of households. We have that 35% of the households are poor. Anyway, they might be vulnerable or not vulnerable, but actually the policy is attending this group of people. But we have to concentrate in this, not poor and vulnerable. And these are the group of households who given the shops, different shops and the idiosyncratic characteristics of the household. They are in and out of poverty. And what we have to stress is that for example, this is the maximum income, $155.3 per month. And these are poor. And they are not poor with the minimum income of $155.60. This 30 cents of difference in income doesn't make any or much difference in the well-being. So for policy purposes, this is poor, but this is not poor. But someone who has in this case 30 cents higher income of 30 cents doesn't make no poor. Probably they have the same well-being characteristics. So this is the group of households that the policy should take care of because they are the potential poor. And there's no difference in well-being. And this is what this estimation is helping to complement the FMO. And what we have, when we combine poverty and modernity, we have here, these are... We see that during these five years, the poverty level hasn't changed too much. 23%, 23%, 24%, 24%. So that means that the tax policy has been consistent through time because of the resources we saw. But we see that the vulnerability through time has been decreasing from 11% to 8% in five years. These are only three points of vulnerability, but statistically significant. So the distribution of the tax policy or taxes has been benefiting to the vulnerable. And so to fight against poverty, we should consider these two household groups. The poor, the ones who are assisted by the normal policy. But this will include yes, the vulnerable. So we will start to assist around one third of the population. So this is because of time we don't go through the water. But what do we have as a conclusion? That micro-simulation can be used to estimate vulnerability to poverty based on cross-sectional data. So in that case, since we don't have an entrepreneurial economic, we can calculate those figures. In conclusion, around 20% of the population is identified as poor and vulnerable. And 25% no poor and vulnerable. And those who have to be considered. In this case, I would say 45% of the population. But based on these results and this model, we have to exploit the advantages of eco-mode. To identify the possible of future poor households they are exposed to all the shocks that can happen in the economy. And this model can account for changes in the labor market in a diverse way. So we have to now cast the model every year and that helps a lot. I know some figures, some pictures, these are biased pictures. We have this, this is Keto, the capital. There's much light, don't see that. We have an idea. Thank you.