 Yeah, so my name is Angela Mensa and this is a project that I'm doing with my advisor, Professor Edward Barby from Colorado State University. So we all know that human capital is very essential to economic growth. Adam Smith was the first to lay the foundation for this idea in his address in 1917. When he said that acquired and useful skills of individuals are fundamental to wealth creation and economic growth, the fact is that this, the idea of human capital was not credited to Adam Smith, but by this statement he laid the foundation. And then we have other people like Roma who argues that for us, for poorer countries to grow, they have to accumulate human capital faster than the richer countries, faster than the initial level they have in order to grow. And Machu also acknowledged that poorer countries have to accumulate human capital faster. So basically Machu and Roma were following up on the steps of Adam Smith and setting up the framework why human capital is very important to economic growth in general. So the research question starts from here when we ask that what happens if human capital is constrained by the environment? The premise is we need human capital to grow as a country or as a world economy. The World Bank estimate that more than half of the world's population is exposed to unsafely managed water and mainly those are from developing countries and about 800,000 deaths accrue to this type of pollution every year. Overall about 24% of global debt can be linked to environmental factors in one way or the other, from air pollution, unclean water and all of that. So the research question is if we have established that human capital is essential for growth and human capital is actually being inhibited by the environment, then how would that affect growth in general and what can we say about inequality? So basically this paper we focused on the hours lost due to environmental mobility and mortality resulting from issues from the environment, from pollution, water pollution, air pollution, sanitation, anthropogenic climate change and ecosystem degradations. So I want to say this on notice that we are focusing on the hours lost. So let me explain that for example if you spend two hours in a hospital because you ate contaminated food, the data account for all those. We are not looking at the actual body count. We are looking at the hours you lose as a result of mobility related things from the environment. And in this paper we call that environmentally related impact on health. But in general it's actually called the environmentally related disability adjusted life years or dailies. So basically this is the framework of dailies, environmentally related dailies on the global scale. You see that lower and middle income countries have higher proportion of environmentally related dailies. The higher income countries are basically their percentage is quite low. When we look at it, even when we look at it in terms of per capita, they are way, way low, very negligible. So we can conclude from this that the effect is disproportionately high in lower and low income countries. So that would have inference for our estimation and what we should see. So the question is what is the mechanism by which this affects? We've established that it affects economic growth. So what is the mechanism by which it will affect inequality? So basically this environmentally related health impact is in two sessions, deaths resulting from the environmental health risk or hours lost. Basically that is going to affect the quantity and the quality of human capital that is available for production. So basically if you spend two hours in the hospital, that is two hours less of wage you could have accumulated. So your income is affected. So based on that we can say that the income distribution of the country will be affected. So the income distribution will be affected as a result of hours lost due to the environment. So if we establish that the income distribution is affected, the variance of the income distribution is what we generally call inequality. So basically there is that link that once the distribution is affected then the variance of the income distribution is affected. Now the question is what can we say we regard to inequality? If inequality is affected by this mechanism, can we say something we regard to inequality convergence? So basically that is what this research tried to address. So the question is what is inequality convergence? The basic tenancies comes from income inequality. Income convergence basically income convergence just say that poor countries will have to grow faster than richer countries in order to converge to a common income or a common group income. All things being equal, economists like that were allowed because it simplifies most of our problems. All things being equal, if income is converging then inequality should converge at the same time. Let's look at that in simple terms. Let's say you're starting from thousand dollars and you are trying to convert to five thousand dollars. When you increase your income, basically you are increasing, you are reducing the amount of inequality you have if you are trying to move towards five thousand. So all things being equal, if we achieve income inequality, income convergence then we should achieve inequality convergence as well. So the two are sort of, the inequality convergence is sort of like a side business to income convergence. So we came out with these two strong hypothesis that having established that the environmental health risk actually affect income distribution and the variance of the income distribution then we can clearly say that countries that are starting with higher level of environmental health impacts should experience lower growth in income. And if that is the case then they will experience lower than the proportionate reduction in inequality. And also, we can also hypothesize that the growth reducing impact, like if a country is growing we expect inequality to reduce as a result. But because of the environmental health risk that reduction that should be coming from income may not be experienced. So basically we will not be able to take advantage of the backwardness or the backwardness may not even exist as a result of the environmental related health risk. So these are the specific research questions. Does the effect of the environmental health risk on human capital which is coming from the income distribution affect the speed of inequality convergence? Does environmental health risk directly affect inequality reduction? Or does it also impact the inequality reducing effect that is coming from income growth? So we use data from the wider companion database and the global burden of disease database. So that lead us to these three empirical estimations. First, equation one, we try to find out is there inequality convergence? Can we say that the literature rebellion has done a lot of work trying to prove that there's some level of within group convergence in inequality. So can we say that there is inequality convergence? So we try to test that and see if our results agree with what the mainstream literature is saying. And then if we can confirm there is inequality convergence then we can move a step further to find out the effect of the environment which is on human capital. So the variable sigma would capture the effect of environmental health risk. And then we also incorporate the income effect. That's the effect of the environmental related impact on health impact our convergence process. So these are the three levels of estimations that we look at. So the dependent variables, the gamma g, that is basically the annualized growth in inequality and then the annualized growth in income which is the gamma mu. And then we estimate these basic equations. And these are our results. So basically the first line here tells us that there is convergence because if you see here the hypothesis we've put forward suggests that if there is income convergence lambda one should be less than zero. That is basically what the convergence idea means. And we already stipulated that environmental related impact on health would worsen the rate of inequality reduction. So we expect a positive result from there. So lambda two should be positive. And basically that is what we find. So when we estimate the three stages of equations you realize that when we incorporate the environmental health risk here we get the plastic in here is actually the three dots. Basically there was not so much space so we just made the three. So that is the significance at the less than 5% significant level. And so basically the environmental health risk have a very significant effect on inequality reduction. And you realize that when we incorporate this the speed of convergence, this is the speed of convergence the speed of convergence increase across both. It is only when we disaggregate it into income levels that we see slight changes which we'll get to in the minute. So we try to also address the issue of endogeneity. Is it possible that initial incident of environmental related health risk and initial inequality are talking to each other somehow? So we address that using the instrumental variable. And the results, the coefficients have improved a lot but then the signs are the same meaning that we are on the same track as far as the two estimators are concerned. So now the exciting part was when we decompose the whole data. This is the data for 176 countries. And when we decompose the data into income groups we realize that the low income groups have a higher speed of convergence which agrees with the literature that the backward, they have to take advantage of the backwardness, yeah. That was developed by, that statement was mentioned by Alessandra who says that once you have initial, your initial inequality is higher then you should grow faster. So the speed of convergence for lower income countries appears to be higher. But then when we look here and higher income countries anyway they have no need to be growing faster to be reducing faster because they are originally starting from a low place. So that makes sense for them to have a lower growth, a lower speed of convergence. And now let's look at what happens to income. Look at that. So in terms of income, we realize that between the high, upper middle income and high income countries income has no effect on whether they reduce inequality or not. And then between the lower and lower middle income countries we realize that growth in income is actually worsening their inequality reduction process. So as they grow inequality is worsening. That is what we see in the data. So basically that literally confirms some of our hypothesis and as unfortunate as that is. Basically that is what we find. Now, so we use the predicted values from these estimations to try to find out, given this parameter estimate how long would it take a particular country to converge to a group mean? So we decide to make the group mean to be, the group mean for high income country. So this here, that is the inequality when we look at all high income countries. That is the mean for all high income countries. So we use a simple compound, a reverse compound interest formula and the predicted values from the earlier equations to find out that if you are growing or you are reducing your inequality at a certain rate how long would it take for you to converge to this group mean of the average of high income countries? And we see some very interesting results. For example, look at Nigeria, in 91 years or 92 years they should be converging to the space of people like Finland and Norway. But once we account for the effect of the environment they have no chance at all. Oh, almost. Right, so and if you look at the raw data it's so surprising. So one thing we can see from this graph is that countries that are reducing their environmental related impacts on health faster tend to grow faster or tend to reduce their inequality faster than their counterparts who don't. So basically that is what we see throughout the data. And look, Benny also probably have no chance. And then Nicaragua, very interesting story. So we have a lot of countries that are doing well because they are reducing their level of environmentally related health risk faster than their counterparts. So that literally shows that they might be converging all things being equal when current levels of inequality reduction persist over time they should be converging to this group mean in 93 years. And that's quite a good approximation. So this is a very conservative estimate. Things could change, right? Things could change. There could be political instabilities and stuff like that that could throw this result off. So that's what we're saying all things being equal. If they continue on the same trajectory this is how long it will take. So now I don't know why that moves so fast. Okay, so basically these are our findings, right? Countries, we realize that countries we hire initial incidents of environmental related impact on health simultaneously they have worsen levels of inequality. But when they reduce the level of environmental health impact faster then they tend to converge much quicker to the group mean than the other counterparts. So basically that is telling us that if we ignore the effect of the environment we will be underestimating the speed of convergence. And also we see that inequality, high inequality causes side by side with income growth in developing countries. And I think Revalion also found something similar in one of his 2018 paper where he said that basically in developing countries we see that high inequality is existing side by side with high income growth. So we don't know why yet. We expect that as you are growing your level of inequality should be reducing but for developing countries that's not the case. So basically we can say that income growth per se is not sufficient to reduce inequality at least in the story of developing countries. Because of the level of environmentally related health risk that is actually impeding the human capital accumulation because they are not able to, already they are deficient in human capital. And the environment is also making the situation worse off. So they are not able to accumulate in our human capital to take advantage of technology that is developed elsewhere to grow. So basically that is what we see. Now what are some of the policy implications? The fact is that countries cannot expect to grow if they don't reduce the environmental related impacts on health. That is one thing that the data shows us. So in the case of developing countries basically when you look through the raw data one thing I would say is that you see that there is some strong heterogeneity in the type of environmental related impacts that developing countries face. Developing countries face things like water pollution related issues, sanitation. So people died due to sanitation related issues. But when you go to develop countries like the US and the rest people died due to temperature variations and stuff like that. So basically it tells you that developing countries their issue is just to fix the infrastructure. Build infrastructure, build toilets, build water basically. And that can wipe out a number of people who are dying each year once you fix the infrastructure. So improve access to water. And also many developing countries are still using firewood, charcoal and those things. And that is cause a lot of air related diseases, right? So what is the big picture? The big picture is that our findings actually reaffirm the need to aggressively target the Paris Agreement and try to achieve them. For example, let's look at this. If the green energy transition which is the SDGs seven by achieving that we might actually eliminate the number of people who are dying as a result of energy related sicknesses like using charcoal and firewood. And once we achieve that we basically improve inequality as a result. So that achieving the sustainable development goals actually is essential for us achieving the inequality. I know inequality is the SDG 10 but basically this is telling us that we have to achieve seven in order to achieve 10. And in terms of clean water, clean water that is SDG six. We want to, if we can be able to build infrastructure, provide clean water and solve the sanitation problem in developing countries, we might actually eliminate over 800,000 deaths that accrue to these countries every year. So basically we are using one stone to kill two birds basically. So this result actually affirms, so you realize that improving economic inequality between countries and improving welfare is really strongly tied to our ability to achieve the sustainable development goals. So basically these are the findings from this experiment. Thank you.