A country’s real wealth is its people. A country’s human development index is a measure of the wealth of a country that looks beyond its GDP. It is a measure focusing on three factors that tries to put together dimensions of human development that is how healthy its people are, their educational attainment and how decent the people live. These are measured by the life expectancy, enrolment and literacy rates and the purchasing power and income of the people, respectively. Although, this index may not be a complete measure of human development, it provides a widened view of how human progress and its relationship with how the people live in a place.
HIV virus on the other hand is commonly known to have more cases on areas where extreme poverty exists like in the African Regions. Although, a place’s HDI may be one of the indicators of poverty, it has not been directly equated to the occurrence of HIV victims.
This particular research would want to look if there is a relationship between the HDI of a country and the occurrence or number of cases of HIV victims. Specifically, it would want to test the hypothesis that there is a relationship between the number of HIV victims of a country and it’s HDI. This means that we would want to know if the lower the HDI of a country, the higher the number of its HIV victims.
The population of the study will be all countries. However, because of the number of countries, the reasearcher will be generating a sample size of 50 using simple random sampling using the table of random digits.
Once the sample has been generated, appropriate mathematical processes will be done.
The data to be used is the data is from the website gapminder.org. Two sets of indicators will be used. These are Adults with HIV (%, age 15-49) and the HDI (Human Development Index) for the year 2011. After the random sampling done, the Table 1 shows the raw data to be used (Estimated HIV Prevalence in Percentage – Ages 15 – 49 and the Human Development Index for the year 2011)
It can be gleaned from figure 1 above that Nigeria has the lowest Human Development Index. We can also say that the countries belonging to the lower bracket in terms of its HDI belong to the third world countries.
Figure 2: Estimated HIV Prevalence % - (Ages 15-49)
It can be gleaned from the graph above that, generally third world countries have higher HIV prevalence compared to other countries. However, we cannot conclude yet if this has to do or if there is a significant relationship between the prevalence of HIV in a country and its HDI indicator.
The following mathematical processes and their equivalent values were generated from the data with the sample size of 50.
Based on the data above, HIV Percentage is is negatively skewed to the right while the HDI almost have a 0 skewness. This just means that although can say that the mean percentage of HIV positive in a country is 2% of its population, most of the countries have lower that .1% with infected HIV because the mode is 0.06.
For the HDI on the other hand, the mean HDI of the countries in the sample size is almost the same as the median, thus we can say that the average HDI score of the countries is at least 0.6.
Based on the values of the standard deviation, we can infer that the HIV % values among the countries vary significantly from one country to another because of the standard deviation of 4.38. The HDI of the countries on the other hand have close values and does not vary much from one country to another.
After performing and solving the correlation coefficient ( r ) between the two sets of data, the -.38 value could be interpreted as a weak negative correlation between the two. This means that, there is a weak association between the % of HIV positive victims and the HDI of the country. The two variables moves in opposite directions. This means that although the association is weak, indeed there is an effect of the HDI of the country to the number of HIV clients they have. The higher the HDI of the country, there is a tendency that they experience lower percentage of HIV positive victims between the age of 15 – 49. This may be attributed to various reasons including lack of education and the prevalence of poverty in these countries.
At t=0.02, and p=0.01, we can conclude that definitely, we have reason to believe that there is an effect of the countries HDI to the prevalence of HIV positive victims in the country.