PSYC4001_A1

Introduction

Economic growth and health outcomes vary significantly between different regions of the world. Among various indicators of economic progress, GDP per capita is commonly used to gauge the economic health of a country and its ability to provide for its citizens. Life expectancy, on the other hand, serves as a key measure of overall health and quality of life within a population. Understanding how GDP per capita impacts life expectancy across different regions can provide valuable insights into the broader impacts of economic growth on public health, and serve to inform policy discussions aimed at improving public health through economic development.

Current Study

This research aims to provide a clearer understanding of how economic progress impacts health outcomes across different continents. To this end, the current study will investigate the relationship between GDP per capita and life expectancy, across both time and different continents, using a subset of data from the Gapminder dataset.

We hypothesize that an increase in GDP per capita will be associated with longer life expectancy, as higher economic growth and development often lead to improved health outcomes. Specifically, we expect to observe a positive association: as GDP per capita rises across different continents, life expectancy should also increase over time.

Data Overview

The Gapminder dataset was obtained using the “gapminder” R package, version 1.0.0 (Bryan, 2023). The data were filtered based on year to yield a complete data set. It is comprised of time-series data, in long-format, about 142 countries for the years 1952 to 2007, collected in increments of 5 years for a total of 12 time points per country. The six variables available in this R package are gross domestic product (GDP) per capita in inflation-adjusted USD, predicted life expectancy measured in years, continent (Africa, Americas, Asia, Europe, or Oceania), country (factor with 142 levels), population size, and year. In total, the data frame is comprised of 1704 rows and the six columns of variables.

Analysis

Three plots were created to achieve the study purpose. A scatterplot was used to illustrate the relationship between global GDP per capita and life expectancy. Combined line plots were then used to comparatively depict trends in GDP per capita and life expectancy across continents. Lastly, A dot-and-whisker plot was used to visualize how GDP per capita predicts life expectancy across different continents.

Figure 1: Correlation between Life Expectancy and GDP per Capita

This scatterplot shows a log-transformed GDP per capita measured as inflation-adjusted USD on the x-axis. The top scale is non-transformed scale of the x-axis. The y-axis shows predicted life expectancy from birth, measured in years. Both GDP and life expectancy data include 12 repeat measurements per country. The repeat measurements represent different years at which they were collected, ranging from the year 1952 to 2007 in increments of five years. Total number of countries is 142 and they were unevenly distributed across five continents. The transparent dots on the scatterplot represent all observations and are superposed by a line of best fit with the spearman correlation coefficient of 0.83. Taken all together, the figure supports the notion that across both continents and the years 1952 to 2007 there is a positive correlation between life expectancy and GDP per capita.

Figure 2: GDP Per Capita and Life Expectancy Trends by Continent

By examining changes in median GDP per capita and median life expectancy over time, we can discern correlations between economic growth and improvements in life expectancy.

The combined graph provides a comparative view of GDP per capita and life expectancy trends across continents from 1952 to 2007 in five-year intervals. The top plot depicts the log10-transformed median GDP per capita, whereas the bottom plot depicts median life expectancy in years. Each continent is represented by a distinct colour in both plots, and the unified legend aids in identifying each trend. This visualization supports the positive correlation between GDP per capita and life expectancy, while highlighting the potential global disparities.

Figure 3: Predicting life Expectancy by GDP per Capita Across continents

A series of linear regression models were fitted for each continent to determine how log10-transformed GDP per capita predicts life expectancy. The results were visualized using a dot-and-whisker plot, which shows the coefficient estimates as dots and their 95% confidence intervals as whiskers for each continent. The plot reveals a positive relationship between log10-transformed GDP per capita and life expectancy across all continents, with varying magnitudes of effect. The x-axis represents the coefficient estimates, indicating the effect size of log-transformed GDP per capita on life expectancy. The y-axis indicates the term “log10(gdpPercap)” for which the coefficient estimates are provided. For Asia, a one-unit increase in log10-transformed GDP per capita is associated with a 14.40 year increase in life expectancy. For Europe, a one-unit increase in log10-transformed GDP per capita is associated with a 14.52 year increase in life expectancy. For Africa, a one-unit increase in log10-transformed GDP per capita is associated with a 13.11 year increase in life expectancy. For Americas, a one-unit increase in log10-transformed GDP per capita is associated with a 22.38 year increase in life expectancy. For Oceania, a one-unit increase in log10-transformed GDP per capita is associated with a 25.08 year increase in life expectancy.

Conclusion

The present study aimed to explore the relationship between GDP per capita and life expectancy across different continents over time, utilizing the Gapminder dataset. The findings supported our hypothesis that higher GDP per capita is associated with longer life expectancy.

The scatterplot analysis demonstrated a positive correlation between GDP per capita and life expectancy globally, with a correlation coefficient of 0.83. This indicates a strong association between economic growth and improved health outcomes.

The combined line plots revealed consistent trends across different continents, showing that increases in GDP per capita were generally accompanied by increases in life expectancy. This pattern was observed across all continents, although the magnitude and rate of these changes varied.

The dot-and-whisker plot provided further insight into the relationship by illustrating the coefficient estimates from linear regression models for each continent. The results indicated a positive relationship between log-transformed GDP per capita and life expectancy, with significant effect sizes observed in all regions. Notably, the effect size was largest in Oceania and the Americas, suggesting that economic growth in these regions has a particularly strong impact on life expectancy.

Overall, these findings underscore the importance of economic development in enhancing public health. By highlighting the positive relationship between GDP per capita and life expectancy across continents, this study contributes to the understanding of how economic progress can lead to improved health outcomes globally.