Over the past two decades, we have seen a significant increase in economic wealth of countries, specifically those that strongly influence global economics. After examining the national accounts data of six countries: Chile, China, Germany, Mexico, United States of America, Zimbabwe, for the years 2000 - 2015, and comparing the data to the countries' corresponding life expectancy, we come to a conclusion that our lives are becoming more "expensive".
We discuss the findings through graphs and charts. Some key terms to understand:
Below, are two graphs depicting the GDP growth over a 15-year period. Although tracking inequality levels worldwide may pose various statistical challenges (e.g. different nations tally income and wealth in different ways, and some nations barely tally reliable stats at all) for researchers, the data shown here are key findings from large amounts of research. The figure on the left provides one graph showcasing each country's GDP in bars that indicate the year, while the figure on the right depicts change in GDP over time in six distinct line graphs (distinguishable by country).
Notice how China's and USA's GDP has grown significantly over the years, whereas Zimbabwe's GDP is almost nonexistent compared to the other countries'. Excluding Zimbabwe, Chile's GDP has had the least amount of change in GDP. The global GDP disparity is causing greater powers to dominate the world's economic ebb and flow, which leads to tension among nations who lack the means to grow as quickly. Wealthy nations thus have a larger say in global policy due to their economic statuses.
Similar in style to the two graphs of GDP, the graphs directly above and to the left display Life Expectancy at Birth in Years by country. Compared to GDP, most countries' life expectancy have not experienced a drastic increase in life expectancy, except Zimbabwe. Although Zimbabwe's GDP is comparatively low, the nation's increase in life expectancy could be due to the influence of growing economic wealth in surrounding nations, inflation within the nation, and population growth.
The figure to the right is a "violin plot", which is a hybrid of a box plot and a kernel density plot. In essence, the graph displays statistics from a box plot (median, interquartile range, etc.) and further shows how the probability density of the data at different values.
Notice how every country, except Zimbabwe, has a life expectancy that is rather centralized and significantly high. The average life expectancy is in the 70s - 80s (years), which is significantly higher than what the average lifespan was in the early 1900s. This can be seen by the short, rounded "violins" on the graph, which correspond with the previous two Life Expectancy graphs. Zimbabwe, on the other hand, has a longer, skinnier violin because it has experienced the greatest change in average lifespan for the 15-year period.
The general trend for both GDP and Life Expectancy has been positive. In all of our findings, we notice that as GDP rises, so does life expectancy. The figure (left) is divided into years, and every year's graph analyzes GDP versus Life Expectancy. The purpose of this image is to tie in all of our findings. To read the scatter plots, locate a country by its color, follow it on the x-axis to track GDP, and follow it on the y-axis to track Life Expectancy. Although the scatter plots are not as easy to read as the bar charts and line graphs provided previously, they truly showcase how GDP directly influences the life expectancy of a nation in one year. Most countries will have moved rightwards on the x-axis and upwards on the y-axis.
There is a strong correlation in GDP growth and increase in life expectancy. The USA and China have boomed economically, but their life expectancy don't necessarily surpass Germany's and Chile's. Zimbabwe's economic status and life expectancy may not be the highest, but there is indication of growth in the nation, which is a positive sign that it is working towards improvement. Although the data we found may not be the most accurate as there could be discrepancies in GDP measurements styles worldwide (this could be addressed in more detail during further research), we find that the data ultimately depicts worthwhile, general trends. As people gain the means have a higher standard of living, they gain access to more nutritious foods, better healthcare, and a safer environment, which ultimately leads to longer life expectancy.