Human development is a process of providing mechanisms through which people can flourish and attain self-actualization in their lives. The rationale of human development as a process goes beyond focusing on economic development as an indicator of people’s wellbeing. It therefore incorporates other dimensions which are crucial in establishing the extent to which people would describe quality life. Traditionally, most economists have used quantitative indicators such as GDP Per capita as a measure of people’s wellbeing in a particular economy.
The reality is however, different because quantitative indicators such as these are limited to assessing economic success of the region where people live but not the success of life of those people living in that region. In human development approach, we include other qualitative aspects that define the quality of life lived by people.
In this report, we shall explore three variables; 1) Mean years of schooling (years) 2012. This is a continuous variable measured in years. It is obtained by getting the average number of years of formal education received by people aged 25 years and above in a given country, 2) Gross national income (GNI) per capita (2011 PPP $) 2013. This is a continuous variable measured in $ (US Dollar) which is obtained by taking the GDP plus the factor income from abroad and 3) Unemployment rate (% aged 15 years and older) 2004-2013, which is expressed as a percentage. It is obtained by expressing the ratio of the unemployed persons to total labour force as a percentage.
Under normal conditions, it is expected that mean years of schooling and unemployment rate have negative correlation while GNI per capita and unemployment rate have positive correlation. The correlation between GNI per capita and mean years of schooling will depend on other factors such as the demographic structures of the population.
We begin our analysis by exploring descriptive statistics of mean age of schooling (years) 2012. This is a continuous and quantitative variable whose distribution can be understood both visually and numerically. There are 187 cases in our study. This variable conveys summary information about the length of time people in each country spends in pursuing formal education. Since education is key in making people knowledgeable and promotes human development by increasing creativity and productivity especially at work places, the data given is in support of this assumption. This is because high average number of years of schooling has been registered in developed countries while low average number of schooling has been registered in regions with high poverty levels such as sub-Saharan Africa.
We can produce a boxplot for this variable. This is a statistical technique that helps use to understand the distribution of the data points at a glance. Using a boxplot we are able to take note of the skewness of the data and detect presence of outliers if any.
Among the countries with the least number of average years of schooling are Burkina Faso, Niger, Chad, Mali and Guinea Bissau while the countries with the highest number of average years of schooling are United States of America, Germany, Australia, New Zealand and Norway.
Next we analyze the second variable; Gross national income (GNI) per capita (2011 PPP $) 2013. This is also a continuous and quantitative variable. We can explore the descriptive statistics of this variable visually and numerically in order to understand the distribution and dispersion of the data values in order to measure the extent to which it is associated with human development.
GNI per capita attempts to quantify the net output of the resources of the country whenever they are located. It is computed by taking the Gross domestic product of a country and adding factor income from abroad. Factor income from abroad is the difference in income from citizens of a given country working outside the country subtract the income generated by foreigners working in the country. Under normal conditions, GNI in developing countries will be less than the GDP because net factor income is always negative and the reverse is true in developed countries.
The boxplot indicated positive skewness indicating that most of the data values in this variables are above the mean. Also, there is evidence of outliers on the right implying that some countries, notably Qatar have unusually high GNI.
Among countries with the least GNI per capita are Democratic Republic of Congo, Central Africa Republic, Niger, Burundi and Malawi while among countries with the largest GNI are Qatar, Liechtenstein, Singapore, Norway and United States of America.
The third variable to analyze is unemployment rate (% age 15 years and older) 2004 -2013. This variable indicates the number of those who are qualified, able and willing to work at the current wages but are still jobless. This is also a quantitative variable expressed as a percentage whose distribution can be best understood numerically. The boxplot of unemployment rate shows outliers on the right. This shows that unemployment rate for the period of time given is positively skewed.
The mean unemployment rate for 149 countries is 9.5% with median as 7.5%. The Mid-Range is 15.7%, standard deviation 6.861%. Range is 30.6% while interquartile range is equal to 7.3%
Among the countries with the highest unemployment rates are former Yugoslavia Republic Kiribati, Switzerland, Armenia and Lesotho while those countries with the least unemployment rates are Thailand, Tonga, Vietnam, Guinea and Bahrain.
We can also analyze variables pairwise
The first pair of variables we can analyze mean age of schooling (years) and Gross national Income (GNI) per capita (2011 PPP $) 2013. We can visually display the relationship between these variables using a scatterplot. Most likely we expect a positive correlation between these variables because both are used to indicate improvement in human development. This implies that the more the years spent pursuing formal education the higher the GNI per capita. This positive correlation is shown by a positive gradient in the scatterplot.
The second pair of variables to be analyzed is unemployment rate and Gross National Income (GNI) per capita. Here, we most likely expect negative correlation because these variables have inverse impact on human development. As GNI indicates improvement in human development, high unemployment rates indicates poor human development.
Evidently, we detect a negative relationship in the above pair of variables. This implies that increase in Gross national income leads to a decrease in the rate of unemployment and the reverse is true in each case. Similarly, this is not a cause effect relationship as indicated by the Pearson-Product moment correlation. The coefficient of correlation r = -0.271. This can be interpreted to mean, an increase in Gross National product by one unit leads to a corresponding decrease in unemployment rate by about 27%.
Finally, the last pair of variables we can analyze if mean years of schooling and unemployment rate. Under normal conditions, we expect a negative correlation between these two variables. This is because, they affect human development in opposite direction. This means that as increase in mean years of schooling have a positive implication on human development due to the fact that it increases knowledge, increase in unemployment reduces human development because it causes dissatisfaction. Therefore, we expect that the two variables will impact negatively on each other.
We can notice that there is an inverse relationship between mean years of schooling and unemployment rates. However, this relationship is very weak. The coefficient of correlation r = -0.044 implies that when one variable increases by one unit, it leads to the corresponding decrease of the other variable by about 5 %. This is a weak negative relationship.
Bearing the idea of human development in mind, it is evident that more than one dimension of people’s lives need to be considered in order to make appropriate conclusions about the quality of life one is leading. Case studies from the data given shows that basing our judgment of people’s wellbeing using economic indicators can be adversely misleading. In this report we considered three variables which are key approaches to understanding human development. The variables were chosen because they have direct impact on the people, choices they make and assesses the opportunities available to them that can enable them lead productive lives.
For the case of most African countries, we noted countries such as South Africa having outstanding Gross national income over years. This could deceive one to think that people living in this region have developed to their full potential. However, it is interesting to notice that the same country records among the countries with the highest unemployment rates. From our discussion of the pairwise analysis high unemployment rate is an indication of poor living standards. The same trend features across most African economies.
Conversely, most countries in the Middle East and Europe have a higher human development despite their ordinary Gross domestic Income. For instance, Qatar has recorded the smallest unemployment rates of 0.4. This means that only less than 4% of its population of the labour force is unemployed. For such an economy, it can be claimed validly that people are have more choices to make and opportunities to develop oneself to full potential are vast.
We note the most countries in Africa has similar distribution of the three variable under study in this report. As noted earlier, Gross national income and mean years of schooling have negative skewness in Africa region. At the same time this region has positive skewness in the distribution of unemployment rates for the period under study. Why? The reason is clear that most Africa countries have their GNI far below the world GNI mean. This causes the distribution of this variable to be skewed negatively. Similar to mean years of schooling.
The positive skewness in unemployment rates indicates that most African countries have average unemployment level above the world unemployment mean. From our discussion, both Gross national income per capita and mean years of schooling improve human development while unemployment rates is undesirable when it comes to human development. Basing on this observation, we conclude that human development need to be measured using more than one indicator because in so doing, we will be able to capture what is really a measure of wellbeing.
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