GDP per capita of the three sub-regions
Introduction
There is always a running debate on what makes the Growth Domestic Product per Capita of a country or region grows. Several variables may help forecast GDP, including population, industrialization, minerals, labor, and many more. In this report, the aim to look at the three regions, mostly with third world countries and a developed region. The topic of investigation will be if the labor force can impact the GDP of a region. The report will also check how the GDP varies across the three regions and, in a similar manner, check how the labor force also varies. The report will feature three regions: Africa (Saharan), Africa (sub-Saharan), and the Middle East.
Investigation 1:
The data will be comparing the mean GDP per capita and mean labor force of the three sub-regions, Africa (Saharan), Africa (sub-Saharan), and the Middle East.
Mode of investigation:
The data will use descriptive statistics, bar charts, and boxplots of the four variables for comparison purposes:
Descriptive statistics:
Explanation:
The data is checking the descriptive analysis of the two variables within the three sub-regions. From the 73 cases of the countries sampled in the data, the data can ascertain that the mean of the labor force in the sub-regions is 7962809.12 with a standard deviation of 1129949.48. From the summary, the data shows that the minimum labor force in the regions is 2486, and the maximum labor force 58800000. Upon checking the dispersion through the interquartile range, 907600 shows a great gap between the countries with low labor force and countries with a high labor force in the combined regions.
Explanation:
The data sought out to check the dispersion of the GDP variable among the three sub-regions. It is clear that the Middle East has fewer countries, but their cumulative mean GDP is high at 35,000, with the lowest being Africa Sub Saharan region with 5000 cumulative means. Africa Saharan is better peaking above 10,000. This shows that even though Africa has many countries, the GDP is not still hence indicating that many countries in Africa actually have low GDP, whereas in the Middle East, the countries are few, but the majority of the countries have very high GDP that masks those that have low GDP.
The data sought out to check the dispersion of the labor force among the three sub-regions. Here Africa Saharan peaks with the highest of the three at 12,000,000, with the Middle east being the lowest at just over 6,000,000. The Africa sub-Saharan is also relatively higher, with 8,000,000. This chart shows that Africa has a great labor force relative to the Middle East, but the GDP is lagging behind. This poses the question as to why the Middle East has the least labor force, yet it has a greater GDP and if there is a correlation or relationship between GDP and labor force.
Explanation:
The boxplots helped the data check on the distribution of the variables relative to the mean. In this first boxplot of the GDP per capita in the three regions, the middle east shows that the greater percentage of GDP is actually on the higher side with even an outlier on the top section. This outlier brought about by Qatar with a GDP of 132100, showing it has a great GDP. African Saharan shows the majority of the GDP lies above the mean. The Africa sub-Saharan shows that it is on the lower side, but with the majority of the countries placed above the mean with even noticeable outliers on the higher side, for example, the starred outlier at 20 belongs to Equatorial Guinea with 31800.
The boxplots helped the data check on the distribution of the variables relative to the mean. In this second boxplot of the labor force in the three regions, the middle east shows that the greater percentage of labor is actually on the higher side with even a starred outlier on the top section. This outlier brought about by turkey with a labor force of 30240000. African Saharan shows the majority of the GDP lies below the mean. The Africa sub-Saharan shows that it is balanced at the top and bottom of mean with even noticeable outliers on the higher side. For example, the starred outlier at 39 belongs to Nigeria with 58800000.
Investigation 2:
The data will be investigating the correlation between GDP per capita and Labour force and regression analysis of the two variables in the three sub-regions. It will check if there is sufficient evidence to use the labor force to predict the GDP.
Mode of investigation:
The data will use a scatterplot diagram, a correlation table, and a regression table
Scatter plot:
Explanation:
The scatterplot shows the relationship between the GDP per capita and the labor force of the regions. The line passing through scattered points shows that the relationship is relatively weak, and also the linear decreasing linear relationship shows that GDP lowers as the labor force decreases in the three regions.
Correlation table:
Explanation:
The correlation table shows that the Pearson correlation lies at -0.119 hence showing that the linear relationship between GDP and labor force is a negative one. The relationship is negatively weak. The sig value shows that there is no significant relationship at the 0.01 level of significance.
Regression table:
Explanation:
This regression table shows if we can use the labor force to predict the GDP of the three regions. By checking on the R-Square segment, the data shows 0.014, which translates to 1.4%. This means that only 1.4% of labor force data can be used to explain the GDP of the region. This is a very low number. Also, on checking the coefficients on the table, the labor force lies at 000 because there the sig value is 0.319 showing that there is no significant relationship to predict the GDP of the three regions. This then proves that the size of the labor force does not necessarily mean a great GDP; there are other factors that actually fuel GDP to growth.
Conclusion
The data hence concludes that the Middle East region has a very GDP compared to the two regions of Africa (Saharan and Sub Saharan), and in contrast, the African regions have a higher labor force than the Middle East. The data then sought to figure out if the labor force affects the GDP and if there is a relationship. The conclusion is that correlation, and regression analysis shows that there is a negative and very weak relationship between the two variables; therefore, the labor force cannot predict the GDP of the three regions.
References
Fauzi, A., & Pradipta, I. W. (2018). Research methods and data analysis techniques in education articles published by Indonesian biology educational journals. JPBI (Jurnal Pendidikan Biologi Indonesia), 4(2), 123-134.
Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.