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 will be to look at the three regions, mostly third world countries and a developed area. The topic of investigation will be if the labor force can impact the GDP of an area. 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. Furthermore, it 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, 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 considerable 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. It shows that even though Africa has many countries, the GDP is not still hence indicating that many countries in Africa have low GDP. In contrast, in the Middle East, the states are few, but the majority of the states have very high GDP that masks those with 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 high labor force relative to the Middle East, but the GDP lags. That poses the question as to why the Middle East has the least labor force, yet it has a higher GDP and a correlation 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 high 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. Still, 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 indicates that the relationship is relatively weak. 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 indicates 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%. It means that only 1.4% of labor force data can be used to explain the GDP of the region. The value is relatively low. Also, on checking the coefficients on the table, the labor force lies at 000 because there is a sig value of 0.319, showing that there is no significant relationship to predicting the GDP of the three regions. It proves that the size of the labor force does not necessarily mean a high GDP; other factors fuel GDP to grow.
Conclusion
The data hence concludes that the Middle East region has a high GDP compared to the two areas of Africa (Saharan and Sub Saharan). In contrast, the African regions have a higher labor force than the Middle East. The data sought to figure out if the labor force affects the GDP and if there is a relationship. It can be concluded that correlation and regression analysis shows that there is a negative and 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.