Water scarcity as an indicator of poverty in the world

Transcription

1 Water scarcity as an indicator of poverty in the world Maëlle LIMOUZIN CE 397 Statistics in Water Resources Dr David MAIDMENT University of Texas at Austin Term Project Spring 29 Introduction Freshwater represents 2.5% of global water resources, and only.3% of this freshwater is renewable. Moreover, this freshwater is not well distributed on the planet as we can observe on the map below. Figure 1 Map of the areas of physical and economic water scarcity in the world

2 Looking at this map and comparing it to a poverty map of the world (Figure 2), we can think that a correlation between those two factors, the water scarcity and the poverty, exists. This correlation is the main subject of this project. Showing that a correlation exists would show that the water scarcity or on contrary the water availability can be used as an indicator of poverty or respectively development. Figure 2 Map of infant mortality rate in the world This project relies also on the different poverty indicators available. In the map above, we can see that the infant mortality is used as a poverty indicator, but others exist. This project analyses 4 different indicators and compares them and their correlation to the water scarcity in the world. The 4 indicators used are: GDP (Gross Domestic Product) Infant mortality rate (the number of deaths of infants (one year of age or younger) per 1 live births) Life expectancy at birth HDI (Human Development Index), it represents the average of three general indices: life expectancy index, education index and GDP index Another poverty indicator could have been usable: HPI, the Human Poverty Index but as the calculation is not the same for developing countries and developed countries, I thought that it would not allow me to compare both types of countries and their correlation with the water scarcity in the entire world.

3 Data I found freshwater withdrawal data from the World Water s website ( as in the following table: As for the indicators, I used life expectancy at birth and infant mortality rate from the World Population Prospects 26 Revision published by the United Nations ( HDI (and HPI but I finally did not use it) data are available in the Human Development Report 26 published by the United Nations Development Program. ( GDP per capita has been found in the 25 International Comparison Program published by the World Bank ( final.pdf ). I was not able to find data from the same year but all these data have been collected in so we will assume that they are comparable. I also used the world shape file for ArcGIS from the following website:

4 The next step, after having downloading all the data, was to compile them in one table. As some of the data were only available on PDF files and not organized in the same way (some reports class the data per continent, some per HDI and some per name), it took me a long time to be able to put everything together. I decided to organize the data in the alphabetical order, as it was in the GIS world shape file, so that my table would be easier to implement in ArcGIS. I had to delete some countries from which I did not have all the information as Cuba, Serbia and Montenegro, Bosnia and Herzegovina, Zimbabwe, etc. I finally ended up with the following table for 149 different countries: Country Pop. (millio ns) Total Freshwater Withdrawal (m 3 /pers/y) Per Capita Withdrawal (m 3 /pers/ y) Domestic Use (m 3 /pers /y) % of population without sustainable access to improved water source HDI GDP (billi ons) GDP per capita (billion s) Infant mortality rate Life expectanc y at birth For my analysis, I first used the total freshwater withdrawal for each country, but some countries consume a great amount of water for irrigation and it changes all the results because the water scarcity would be more oriented on a domestic use. So then I used only the domestic use that was available in WaterWorld data to see if the results were better. That is why the following work is divided in two parts. In these two part, I finally did not use the % of population without sustainable access to improved water source in my calculations because data are available only for the poorest countries but I kept it in my table because I think these numbers are really interesting as we will see on a map later.

5 First part: Total freshwater withdrawal per country As said before, looking at the freshwater withdrawal per capita, the data seemed unrealistic in some countries because of the irrigation use of the freshwater withdrawn. For example, the freshwater per capita withdrawal (m 3 /pers/y) for the United States of America is 1,6, for France it is 548 and I found for Turkmenistan: 5,14, for Azerbaijan: 2,51, for Hungary: 2,82, for Ecuador: 1,283 and some other big numbers for countries that are not really considered developed. First, I did not know where those curious numbers came from so to be able to still do something with the data I had, I decided not to take into account the countries that are not the most developed countries in the world and have a freshwater withdrawal per capita greater than 1, m 3 /pers/y. This leaves us with 132 countries, which is still a consistent dataset. Trends (Simple Linear Regression) I used Excel trendline tool to create some trends in order to see if one of my selected poverty indicators would show more obvious correlation than the others. We can see, as expected that the population and the total freshwater withdrawal are showing a quite good linear regression. The more people live in a country, the more freshwater this country withdraws. Total Freshwater Withdrawal y =.5111x R² = ,. 1,.. Population

6 Concerning poverty indicators, simple regressions do not show the same expected trends. The trends are increasing or decreasing when expected, but the R square is very small, never above.3, so the trends shown cannot be considered significant. Freshwater Withdrawal per Capita 1,6 1,4 1,2 1, y =.55x R² =.833 GDP per Capita Freshwater Withdrawal per Capita 1,6 1,4 1,2 1, y = x R² = Infant Mortality Rate

7 Freshwater Withdrawal per Capita 1,6 1,4 1,2 1, y = 15.15x R² = Life expectancy at birth Freshwater Withdrawal per Capita 1,6 1,4 1,2 1, y = x R² = HDI Those trends are simple linear regression. To be able to take into account several variables at the same time, we have to use multiple linear regression, especially to compare the total freshwater withdrawal per country and the poverty indicators because we have to use also the population variable. However, before going further into the regressions, I was willing to study the outliers of this dataset.

8 Outliers The only trend that had an R square enough significant to be studied was the first one (Total freshwater withdrawal vs. Population). To define the outliers, I used the method described in Helsel and Hirsch (p.246), leverage is a measure of an outlier in the x direction. A high leverage point is one where, where p is the number of coefficients in the model and n is the number of data use. The idea is to check the degree of deviation of an individual point from the regression line in the x and y directions with this value. For deviation in the x direction, the statistics h i is computed as: Where SS x is the sum of the squares x. 1 For deviations in the y direction we use the standardized residual e si. It is the actual residual divided by its standard error, S e. The estimated y can be calculated using the trendline equation. Alternatively, the residual can be found in the residuals output of the regression analysis. Then 1 Where the s in this equation is the standard error of estimate of the regression equation. Helsel and Hirsch describes an extreme outlier as one for which e si >3 but in order to only get rid of only the most extreme outliers I just decided to use the ones for which e si >6. The only one that I found was the United States of America. This means that regarding the total freshwater withdrawal vs. the population of a country, the only country that seems to be significantly distant from the rest of the data is the USA. Indeed, Americans seem to use a lot more water per capita than in the rest of the world. It may be confirmed in the following part with only taking into account the domestic use. If we plot again the freshwater withdrawal vs. the population, we get a really better regression with an R square of.95 compared to an initial value of.85.

9 Total freshwater withdrawal 1, y =.4834x R² = ,. 1,. Population Regressions (Multiple Linear Regressions) I used the Regression Tool in the Data Analysis Toolpack available in Microsoft Excel to do the multiple linear regressions. The results of the multiple linear regressions I did are reported in the following table. For each variable, the first column is the coefficient in the regression and the second column is the t statistic of this coefficient. I highlighted all the coefficients that are statistically significant. We can see that the result does not appear to be really interesting: out of our 4 poverty indicators, two appear to never be significant and the two others may be significant. The HDI seems to be the more significant poverty indicator in this case. That is what could have been expected because the HDI take into account several degrees of poverty or development.

10 Negative values were expected for the infant mortality rate as the mortality rate is bigger when the poverty is bigger and when the freshwater withdrawal is expected to be smaller. Concerning the GDP per capita, a negative coefficient is not expected but as the coefficient appears not to be significant, it does not really matter. Maps In order to see if my first impression about the correlation between the two maps used in the introduction was also represented by my data even if the regressions did not show good results, I decided to create maps of my data using ArcGIS. Countries in grey are the countries for which I did not have data or which I did not take into account because of the odd numbers I found. Figure 3 Map of the world: Freshwater withdrawal per capita

11 Figure 4 Map of the world: GDP per capita Figure 5 Map of the world: HDI We can see on those maps that even if they are not absolutely the same, the feeling that they affect each other is obvious. Particularly between HDI and freshwater withdrawal, we can see a significant similarity. As said previously, I did not use the % of population without sustainable access to improved water source in my analysis but I thought that just showing a map of these data could be interesting given the relevance of these numbers. So here are two maps of the % of population without sustainable access to improved water source. We can see again that, as expected, the countries that have a more difficult access to water are the poorest countries.

12 Figure 6 Map of the world: % of population without sustainable access to improved water source Figure 7 Map of Africa: % of population without sustainable access to improved water source

13 Second part: Only domestic freshwater use After having understanding that all the odd numbers I found were because of the big part of irrigation in some countries, I did the same study with all the countries using only the domestic use of the freshwater withdrawal in each country. Trends I looked again at the different trends to see if any improvement was clearly visible. Domestic freshwater withdrawal y = 4.15x R² = ,. 1,. Population Again, the correlation between the total domestic withdrawal and the population of each country shows an R square rather high. It is unexpected that this time the R square found is less than the previous one, but it could also said that the population is not the only factor to influence the domestic freshwater withdrawal and the other factors influencing it could be poverty indicators. 1 Domestic freshwater withdrawal y = 4.855x R² = GDP 1 1

14 The simple linear regression between GDP and domestic withdrawal has an R square almost as high as the one between population and withdrawal. This is encouraging because it means that the GDP affects the domestic freshwater withdrawal almost as much as the population of a country. These results are a lot better than the first analysis without using only domestic withdrawal. Only with two trends, we have been able to show the interest of this new analysis compared to the previous one. Now, let s see if the domestic freshwater per capita and the other poverty indicators selected show the same encouraging results. Domestic freshwater withdrawal per capita y =.1x R² =.752 GDP per capita Domestic freshwater withdrawal per capita (5.) y = x R² = Life expectancy at birth

15 Domestic freshwater withdrawal per capita y =.811x R² = Infant mortality rate Domestic freshwater withdrawal per capita y = x R² = HDI The only interesting improvement in those trends is for the HDI. R square is almost.4 which is not really high but better than in the previous part. We can, thus, expect an improvement also in the multiple linear regressions.

16 Outliers Again, as in the previous part, I have been interested in looking at the outliers. I used the same method and here again, the only extreme outlier is the United States of America. So even when taking into account only the domestic use, the United States of America seem to consume a lot more than any other country regarding their population. 7 Domestic freshwater withdrawal y = 4.15x R² = ,. 1,2. 1,4. Population When getting rid of the US, the R square goes from.6 to.81, as we can see on the following plot, which is a significant improvement. Domestic freshwater withdrawal y = 36.59x R² = ,. 1,2. 1,4. Population

17 If we lower the limit for the extreme outlier to e si >3, we have to get rid of the China also. And then the R square becomes.85, a little better than previously. Domestic freshwater withdrawal y = 47.55x R² = ,. 1,2. Population So China and United States of America can be considered as the two extreme outliers of this dataset, but in a different way: China consumes not enough water given its population and USA consumes too much water given its population. If we look at the plot domestic freshwater withdrawal vs. GDP, we can do the same outlier analysis. Domestic freshwater withdrawal y = 4.855x R² = GDP The outliers calculations give this time, taking the limit as e si >3, China and India as outliers. They have a GDP very low compared to their domestic freshwater withdrawal. If

18 we delete them from the dataset, we end up with an R square of.84 compared to an initial value of.56. Domestic freshwater withdrawal y = x R² = GDP Regressions I used multiple linear regressions again on the domestic dataset. Here are the results: Again, the yellow cases are the statistically significant t stats. The results of these regressions are a lot better than the previous ones with the total freshwater withdrawal. As we can see, all the poverty indicators appear to be statistically significant at least in half of the regressions, except for the GDP per capita, which is a great progress.

19 The GDP seems to be the most significant one concerning total domestic freshwater withdrawal (the R square of the first regression is.89) and looking at the results per capita HDI has a t stats of 8.4, so this is a really significant variable, as expected. The fact that for the total domestic freshwater withdrawal, the GDP is truly more statistically significant than the HDI shows that regarding the total freshwater withdrawal the GDP influences it really more than any other poverty indicators. I did not put the results in the table but if we look at the regression between all the 4 poverty indicators and freshwater withdrawal per capita, the only significant variable appears to be the HDI. As said before, this is what could be expected because the HDI takes into account different degrees of poverty (life expectancy index, education index and GDP index). That explains also why the GDP per capita is not significant when used at the same time as the HDI. The GDP index is already included in the calculation of the HDI. The following plot is a comparison between the real and calculated domestic freshwater withdrawal for each country, taking into account both population and GDP. We can see that the calculated withdrawal is close to the real value. 1 1 real calculated Domestic freshwater withdrawal

20 Conclusions The main conclusion of this project is that the water scarcity in the world can be considered as an indicator of poverty given the correlation we found between several poverty indicators and the domestic freshwater withdrawal in each country of the world. Indeed, this has been shown using multiple linear regressions and having significant coefficients and trends. Another conclusion that can be drawn from this project is that an index as HDI, which takes into account different levels of poverty, is more relevant than simple poverty indicators, even when compared to the water scarcity. Things I learnt doing this project is that, first, compiling data from different sources is not an easy job and second, we have to be very cautious with the data we use, what they tell us and how to interpret them, my first analysis was not right to show what I wanted. Another thing that seems important to me to point out is the difficulty to prove with statistics something that can seem obvious for everybody.