Growth Domestic Product per Capita
Introduction
There has been 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. This report aims to look at 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 a place. Also, the report will check how the
GDP varies across the three regions and, correspondingly, check how the
labor force also varies. The three areas that will be featured are Africa
(Saharan), Africa (sub-Saharan), and the Middle East.
Investigation 1:
It will involve the comparison of 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:
Descriptive statistics, bar charts, and boxplots of the four variables will be used for comparison purposes:
Descriptive statistics:
Explanation:
The data is used to check the descriptive analysis of the
two variables within the three sub-regions. From the 73 cases of the countries that were 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 is 58800000. Upon checking the dispersion through
the interquartile range, 907600 shows a considerable gap between the countries
with low labor force and nations with a high labor force in the combined
regions.
Explanation:
The data was 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 has a low GDP. In contrast, in the Middle East, the states are few, but
most of the states have very high GDP.
The data was used in checking 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 in checking 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 is brought about by Qatar with a GDP of 132100, showing that it has a
high GDP. African Saharan shows that 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.
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 is 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 correlation
between GDP per capita and Labour force and regression analysis of the two
variables in the three sub-regions will be investigated. It will be determined
whether there is sufficient evidence to use the labor force to predict the GDP.
Mode of investigation:
The data will be analyzed using 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
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 relatively weak. The sig value
indicates that there is no significant relationship at the 0.01 level of
significance.
Regression table:
Explanation:
This regression table determines if the labor force can
be used to predict the GDP of the three regions. By checking on the R-Square
the 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
Hence, from the above data, it can be concluded 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
between the two variables. Moreover, correlation and regression analysis show
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.