Analysis of Economic Variables using the Gapminder World Website

Posted: August 25th, 2021

Student’s Name

Instructor’s Name

Course

Date                                                                     

Analysis of Economic Variables using the Gapminder World Website

Graph 1

• Babies per woman (total fertility) vs. Income per person (GDP/capita, PPP$ inflation adjusted) for 1810-2015.

From the graph, it is evident that the fertility rate, which is measured by babies per woman, increases with a decrease in income. The regions with extremely low income per capita experience high population growth due to this high fertility rate. In the graph, the blue scatter dots are associated with low income but a large number of babies per woman. They represent Africa where poverty, unemployment, and illiteracy have limited economic growth, and therefore, limited income per capita. The red scatter dots, which represent Asia, demonstrate moderate income per capita, and a moderate number of babies per woman. Notably, most of the emerging economies are in Asia, which explains the moderate observation. The yellow and green scatter dots represent Europe and America respectively. Both are first world continents with high income per capita. The number of babies per woman are relatively lower compared to Africa and Asia. This explains why The African and Asian population has been increasing tremendously but in both Europe and America, population growth seems to have stagnated. This is an observation which is made after playing the movie slider. From the graph income per capita has a negative correlation to babies per woman. In 20 years’ time, it is likely that the number of babies per woman will decrease following the rising income per capita in most of the developing countries. In my opinion, it is likely that the number of babies per woman will decrease due to other factors far from income, for instance, the use of contraceptives and increased awareness of family planning in developing countries.   

Graph 2

• Income vs. CO₂ emission for 2014.

In most cases, industrialized nations are the worst polluters of the environment. These industrialized countries have the highest income per capita. The fact that they pollute the most means that high income is correlated to high rate of industrial activity and higher rates of pollution. The graph, which plots income against carbon dioxide emission demonstrates this correlation. The blue scatter dots are denser inside the quadrant with low income and low CO₂ emission. Somalia has the lowest income and the least CO₂ emission. On the other extreme, Qatar, which is an Asian country, has the highest income, and consequently, it emits the largest volume of CO₂ emissions. Both green and yellow scatter dots, which represent America and Europe continents, are denser in the quadrant with moderate income and CO₂ emissions. As an emerging continent, especially economically, the majority of countries in Asia have low to high income as well as low to high CO₂ emissions. This explains the presence of the red scatter dots across all quadrants. In 20 years’ time, CO₂ emission will increase as countries become more industrialized based on the positive correlation between income and CO₂ emission. I think the only factor which could make this projected increase fail to happen is the introduction of renewable sources of energy that do not emit CO_2.   

Graph 3

• Exports (% of GDP) vs. Number of people in poverty (% people below $1.25 a day) for 1978-2011 for China and India only.

Increase in exports as a percentage of GDP reduces the trade deficit. This is common in developed economies where exports exceed imports, leading to a trade surplus. This raises per capita income, which in turn, decreases poverty as depicted in the graph. In 1981, India started experiencing an increase in the number of people living below $1.25 per day. This is extreme poverty. The increasing trend continued until 2004. Between 1981 and 2004, exports were increasing only marginally. Between 2005 and 2011, the number of people living below $1.25 per day started decreasing rapidly, while a substantial increase in exports was noted.     In the case of China, the trend was unique. Between 1981 and 1986, the number of people living below $1.25 per day decreased rapidly only to start increasing again between 1987 and 1989. It, however, started decreasing rapidly and consistently up until 2011. During this period, exports were increasing faintly but consistently. A marginal change in exports results in a huge change in poverty levels. This implies a strong correlation. In 20 years’ time, the poverty levels are expected to decrease given that as globalization intensifies, exports increases. I, however, feel that such a trend will only be realized if other factors, for instance, economic disparities, are kept constant.