Assignment 1: How does Country and GDP per Capita Influence Life Expectancy
Braedon Frank & Suchitra Nair
July 24, 2024
- Add bibliographies: R Core Team (2016).
- Add bibliographies: Gapminder (2012).
- Add bibliographies: Linden and Ray (2017)
- Add bibliographies: “Quality of Life Indicator: Gross Domestic Product (GDP) Per Capita” (2024)
Introduction
Research Question
Using the Gapminder data, we wanted to understand what factors contribute to life expectancy and how these factors have changed throughout the 50 year period that the data records. GDP per capita positively correlates with life expectancy from 1952 - 2007, with this relationship being moderated by the continent or geographic region.
Rationale
An economics study by Linden and Ray (2017) suggests a positive relationship between GDP per capita and life expectancy, however they suggest an inequality effect exists, where developed countries such as those in Europe or the Americas may skew this relationship. This means that other countries may not follow this trend, and could have disparities in GDP per capita or life expectancy (Linden & Ray 2017).
Methods and Data Characteristics
Characteristics of the Data
This assignment looks at the interaction between three key variables:
Continent: It is a nominal-categorical variable, comprising Africa, Asia, Americas, Europe, and Oceania. This variable is the first independent variable.
GDP per capita: It is a continuous-numeric variable that measures the average economic output per person in a country. It’s determined by dividing the total GDP, which quantifies the monetary value of all goods and services produced within a nation over a defined period, typically a year, by the country’s population (Statistics Canada, 2024). This variable is the second independent variable.
Life expectancy: is a continuous-numeric variable that measures the average number of years a person is expected to live. This variable represents an average value across the entire country. This variable is the dependent variable.
Methods
To answer our research question, three plots were created. The first plot shows the average life expectancy across every continent during this time period. The second plot shows how the average GDP per capita and life expectancy have changed over this time period and the relationship between the two factors. The third plot shows a distribution of 142 countries which are categorised by continent. The plot compares the 50 year percentage changes of GDP per capita and life expectancy to see how the continent interacts with the relationship shown in plot 2.
Plot 1
Explanation
This plot consists of multiple histograms, each histogram representing a single continent distinguished by its own distinct colour. The histograms present the average life expectancy of the continents over the period (1952-2007). This graphical representation enables a clear presentation of life expectancy distribution across continents during these five decades, allowing comparison between continents. For this plot, continents such as the Americas and Europe have higher average life expectancies when compared to continents such as Africa. In our analysis, we merged Oceania with the Americas due to their shared historical, demographic, and environmental characteristics. Oceania has only two countries, which are too few to be included on their own.
Plot 2
Explanation
In this plot, we seek to represent the data through a dot plot, to help visualise the relationship between GDP per capita and life expectancy based on the global averages calculated across all continents from 1952 to 2007.
The dot plot reveals a clear positive correlation between average GDP per capita and average life expectancy across the period from 1952 to 2007. Each data point represents the global average values for GDP per capita and life expectancy during this time span. Observing the plot, we can discern a trend where higher GDP per capita generally corresponds to longer life expectancy. The peak values observed in 2007 signify the culmination of economic development and healthcare advancements, contributing to the highest recorded levels of both GDP per capita and life expectancy that have grown when compared to the 1952 baseline.
This plot establishes a positive relationship between GDP per capita and life expectancy, and shows how both factors have increased linearly in the 50 year period when averaged across the global population.
Plot 3
Explanation
This plot is a three dimensional scatterplot illustrating the percentage change in GDP per capita and life expectancy from 1952 to 2007, grouped by continent. The continent groups are represented by the colour of the dot.
In this chart, increases in life expectancy generally reflect improving conditions and better access to medical care. An increase in GDP per capita represents a more productive and efficient economy, as the average person contributes more to the GDP than before (Statistics Canada, 2024).
Looking at this plot, the general positive relationship seen in the second plot is not evident, with points distributed seemingly randomly across this plot. This is caused by the introduction of continent as a variable which shows this variable may moderate this relationship, which is in line with the findings of previous literature (Linden & Ray, 2017)
To discern the possible effects of the continents on this relationship, clustering of data may reveal the influence of this variable.
Clustering
The first cluster consists of countries from Africa shown in red. A majority of these observations are clustered vertically on the left. This indicates that these countries have had varying increases in life expectancy, while keeping a consistent GDP per capita. This trend could possibly be explained by low economic growth and political instability.
The second cluster consists of countries from Europe shown in purple. This cluster is the opposite of the first cluster, as it is spread horizontally on the bottom. This means that these countries have had large increases to their GDP per capita, while having minimal growth to their life expectancy. This is likely attributed to life expectancy being already high in the 1950s, with minor increases accounting for small increases in percentage. During this period, Europe continued to experience large economic growth which is reflected in the data.
The third cluster consists of countries from Asia shown in green. This cluster presents in a curve centred at the top right of the graph. This means that countries from Asia have seen the highest increases to GDP per capita and life expectancy. In the 50 year time period, many Asian economies saw rapid expansion, and a large reduction in poverty which is in line with the changes in both variables.
There was no cluster seen with America and Oceania, being generally spread across the chart. This is likely due to the huge disparity between north and south America, with countries such as Canada and America similar to Europe, while central and south American countries were similar to countries in Asia.
When paired with the second chart, it becomes clear that some continents have had disproportionate increases in both GDP per capita and life expectancy, despite the general trend of both increasing proportionately.
Conclusion
Though the trends that are highlighted by these charts may be circumstantial due to the lack of statistical testing, it is possible that continent and geographic region influences the 50 year positive growth between GDP per capita and life expectancy. The first chart shows that average life expectancy is very different between continents. The second chart shows a positive relationship between GDP per capita and life expectancy that increases throughout time. The third chart shows certain continents are clustered, reflecting disproportionate differences in GDP per capita and life expectancy. More research could help explain this trend, or determine why certain continents influence this relationship disproportionately by looking at differences between countries.