 So it is a chapter of my dissertation and the work is still on in progress and I try to study the effect of socioeconomic inequalities in access to basic water drinking water on childhood mortality. So I applied a multivariate fixed effects panel approach in this study and I looked at the law and middle income countries as a country of the study. So basically this panel data covers like 18 years of time span and let's look at the content. So basically this is the content for any standard to research. So basically I will introduce the research problem in GAP and then followed by the research approach to study study of child mortality and then I will describe the goal of the research and hypothesis together with the payoffs and then we'll deep dive to the research design and then we'll interpret the results and then it will be concluded. But before the conclusion I will provide some sort of ideas on limitation of the research. So water access inequality and health this specific relationship has been understudied in the literature so I am very much interested in understanding is there any mechanism that really affects the child health in this perspective. And then the research problem that I'm considering as obviously child mortality but specifically it is caused by diarrhea infection. So I'm not going into the another types of mortality reasons and then the key factor that is under question is obviously water access inequality in this specific framework and these following gaps are identified basically the health and water literature has been understudied and specifically from this inequality perspective and then there is a little say on the factor and the question from the economics perspective as well if you look at the research problem in the wider literature and then there are some plausible biological pathways between water and child mortality therefore I wanted to more look into the specific effect within these two variables specifically from the water inequality as bringing it as a factor and that question. So yeah if you look at the approaches to mortality studies it can be grouped into four different approaches epidemiological and demographic approaches and historical approach as well as interdisciplinary approach and the economic approach the health economics usually usually follow this way. So by adapting economic approach the paper aims to explore the likely relationship between water inequality and child mortality caused by diarrhea specific infection and in the resource poor sittings. So by controlling for the relevant socioeconomic and public health determinants. Therefore I ask the question whether and extent to which do socioeconomic inequalities in access to water impact the child mortality age under five caused by this diarrhea infection in low middle income countries. So hypothesis is that there is a strong correlation and the following payoffs perhaps offered in this specific research framework. So I applied the concentration index method in order to quantify the inequalities in water access and it allows for cross-country analysis and it also offers important value in addressing some of the non-monetary equality problems inequality problems across low middle income countries although it's not holistic and it also highlighted the transmission channel between water inequality and the child mortality and it also addressed the likely effect on the specific cause of child mortality which is very important to highlight because of the consideration of water as the main affecting factor that is this plausible biological pathways between these two variables. So as well as using combination of new and old data sets these are publicly available and this international development community is very much putting an effort to make this data accessible and publicly available for the researchers as well as policy makers. Therefore I think this is an important attempt to make this kind of mission possible. So I put my research design into this table. So if you look at the indicators there are six different indicators starting from the child mortality caused by diarrhea. This is the outcome variable and then it followed by the water access in your quality which was actually been estimated by myself using the JMP data sets and then there is also the control variables including income and income inequality as well as two health policy variables. These are health expenditure and the number of the physicians. So this income starting from income to health policy variables these control variables were collected from the World Bank World Development indicators and the water access inequality was estimated by myself but based on the JMP water data set and child mortality caused by diarrhea is that outcome variable and it is collected from the international health inequality evaluation data set. So yeah we see the data over here we have just mentioned about everything in detail. So why this data? I considered forming points for the selection and it started with availability and accessibility as well as the reliability. Since there is no perfect reliability in everything in this world I wanted to highlight that this is relative reliability. And the second it allows for analysis on reach panel data structure so approximately over 1000 observations have been included and IHME data offers better coverage compared to WHO data in terms of child mortality and it has been like previously unavailable now the IHME data offers it. And this specific IHME data allows for analysis specifically caused by diarrhea on child mortality. So these are the reasons that I selected these data sets. So let's go to the empirical model specification. Basically fixed effects model was adopted and you can see the variables and since we have discussed the variables just before just the previous slides I don't want to repeat it. And estimation of what I have mentioned that this CI what variable has been submitted by myself. So the estimation of water inequality indicator has two considerations. Like what category of water access to be selected, what measurement approach to be applied. So the decision was basically the basic access to drinking water in terms of JMP classification. It's over here. We wanted to go for the safely managed ladder however the data structure couldn't allow it. It's largely not available. Therefore the second best ladder so-called basic was opted for the analysis and when it comes to the measurement approach applied we selected concentration index approach and it has been widely applied in health inequality research. So learning from the another disciplines is also important for me to understand or to introduce new approach to this specific research. And yeah and there is data availability and comparability as well as standardization. These allows for better reliability therefore it was another consideration and applicability of the method. So data and applicability of the method where the two main considerations why I choose the concentration index approach. So access to drinking water in the Democratic Republic of Congo it was as a design of it's basically result of this estimation and estimation of the concentration index for each and every country that has been covered in this study. So as an example of this countries I show this Republic Democratic Republic of the Congo. So in this concentration curve we can see that the cumulative proportion of population with access to water it is on the y-axis and on the other hand the cumulative proportion of population ranked by wealth index is on the x-axis. So further the inequality further the concentration index goes from the inequality line the greater the inequality. So the greater inequality actually lies for the least the worst of population group. So this is for the all the available data sets so you can see that the concentration curve of the access to water is pretty much more concentrated on the better of population rather than the poorer population. So we see that there is a descriptive statistics it says that the higher value of concentration index the higher the mortality rate. So water inequality and mortality rate are significantly closely these are correlated and the significance you can see over here which means the first variable say I want on the fixed effects model it has relatively approximately like 1.5 percent. Yeah the elasticity shows the 1.5 percent so basically it means that the percent 1 percent increase and decrease in water inequalities of earth child mortality by 1.5 percent per thousand live births on average. So other variables show the confirmatory results mostly in line with the literature. So in order to interpret the results I have considered the multicollinearity heterocstacity and model fit. So after considering these things I choose the fixed effect model over the random effects model and on the other hand the fixed effects model offers important advantage of dealing with the unobserved effects compared to the oldest model therefore the final selection was the FE model. So there was one assumption that has to be held in order to make the decision this was the key explanatory variable shouldn't be time constant. And yeah limitation when it comes to the research design and its limitations with there is always some sort of problem and I would I would emphasize that the heterogeneity problem could be said and it doesn't actually account for the country specific variations in this model. However like the main criticism was about this country specific variations but however there is a very practical data constraint this is aggregated to country level therefore I cannot really go into the multi-level analysis and FE model was opted. And on the other hand I relied on FE model just because there is unobservable variables are embedded the unobservable factors are embedded in this model so this was the another point that I relied on. So yeah the practical consideration that some literatures emphasize that FE model is pretty much customary but there are new better approaches are coming. And as a conclusion I have quantified 50 law middle-income countries by utilizing cross-country panel data covering the period between 2000 and 2017 and then the fixed effects model was implemented in this analysis and in order to allow the analysis I estimated the concentration index of water variable was estimated and then the findings revealed that there is strong positive relationship and between the two variables under examination it's also important to recognize that access to adequate water services is often concentrated among the better of population rather than the poorer population groups. So this suggests that the intervention aimed at reducing water access inequalities among the population should be promoted however it should be more targeting to prop or scaling up and this calls for more distribution of investment targeting to the poorer population. And there is another point that I wanted to actually say this was not even included in this. Okay so there is one important point that I really would like to raise but unfortunately it was excluded from my conclusion. This is about it is actually related to the investment. So how actually we need it is actually related to investment and it is about the policy question. So how do we target the investment like from this water access investment in building water infrastructure. The beneficiaries of this investment is often happened to be the better of population because richer households or richer individuals can afford higher paying housings rather than the poorer ones. Therefore they have always resulted in better access and better living conditions. So the international investments in this line of projects they are actually trying to target proper scaling up but in the reality it has been often been resulted in better of population. So this calls for further investigation in my opinion so I would like to end my presentation here.