GDP is Outdated: We Need a Better Alternative
By: Shiyu Murashima, Austin Ma, Joyce Jeon, Audrey Tey, David Spector, Robi Chatterjee
Introduction
GDP has long been the favorite for the measuring and ranking of economic performance and development of countries. However, in recent years GDP has come under fire as many have questioned the validity of GDP, specifically as a global measure of human well-being and the development of countries.
GDP as a formula adds together consumption ©, investment (I), government spending (G), and net exports (NX): GDP = C + I + G + NX. Simply put, GDP accounts for the spending on imports and exports.
As a measure of how much money a country has, this formula is valid by itself. However, GDP does not properly account for a plethora of different factors including the distribution of wealth, leisure time, gender inequality, climate change, and much more. Thus, this raises the question:
Is GDP the wrong global measure of development and prosperity? And is there a better alternative? To dive deeper into this idea, we looked into multiple alternatives from the commonly used Human Development Index (HDI) to more specific measures such as the happiness scores from the World Happiness Report (WHR) and Green Growth.
GDP and HDI
The goal of the Human Development Index (HDI) is to further understand the development of each country using factors that GDP or other economic metrics do not account for. These factors include health, life expectancy, education, and standard of living. To get a better understanding of how HDI is related to GDP per capita, we ranked countries based on these indicators and compared them.
In the map above, we can see the distribution of GDP per capita rankings in 2019. Not all countries are represented in this map due to missing HDI scores. The countries represented will be taken into account when evaluating the correlation between GDP per capita and HDI rankings.
With a correlation coefficient of r = 0.969 (rounded off to 3 significant figures), there is a strong, positive, and linear correlation between the GDP per capita and HDI rankings. The correlation is statistically significant as it has a p-value of less than 0.05. We can see that countries with a higher GDP per capita tended to have higher HDI scores in 2019.
There were also more outliers lying to the left of the regression line, indicating that there are certain countries with higher GDP per capita but relatively lower HDI scores. However, these include only a handful of countries and do not significantly affect the correlation.
GDP and Happiness Scores
Another alternative measure to GDP is the happiness scores from the World Happiness Report (WHR). This indicator measures factors that influence the quality of life to get an understanding of each country’s general well-being. In order to see the relationship between happiness scores and GDP per capita, we looked into the correlation between happiness and GDP per capita rankings.
With a correlation coefficient of r = 0.836 (rounded off to 3 significant figures), there is a positive and linear correlation between GDP per capita and happiness scores. The correlation is also statistically significant as it has a p-value of less than 0.05. This correlation, however, is evidently not as strong as that between GDP per capita and HDI rankings.
In this graph, the data points are relatively sparse and lie further from the regression line. There are also more points that are clear outliers such as Botswana on the far left, with a GDP per capita ranking that places it somewhere in the middle, but a happiness score ranking in the bottom five. We can deduce that happiness is less closely related to GDP per capita. This hints that there may be other factors apart from GDP per capita and economic indicators that have a larger impact on, and have a larger role in predicting the happiness of citizens in a country.
GDP and Happiness as a Function of Time
The previous scatterplot shows how GDP per capita may not be a critical factor impacting a country’s happiness, especially for countries with moderate GDP ranking. Nevertheless, countries with higher GDP rank overall still had higher happiness ranks than countries with lower GDP rank. To examine this deeper, the following visualizations focus on the top 10 countries ranked by GDP. Secondly, the changes in scores are examined, rather than absolute ranking scales. Doing so brings in time (in years) as a third variable for the relation between GDP and Happiness. The reasoning behind this was to detect more subtle nuances, such as whether countries with an upward trend in GDP see a proportional upward trend in happiness scores.
Note: The y-axis was standardized on the same scale for both GDP and Happiness in the original data set.
It seems that for most countries, the direction of change (increase or decrease) is consistent for both GDP and happiness scores. Moreover, it is reassuring to see how happiness was mostly increasing despite the impacts of COVID-19. However, it does not seem like changes in GDP are proportional to changes in Happiness. For example, China saw a relatively small GDP increase compared to the large increase in happiness.
When looking at the data from 2017–2019, all 10 countries’ GDP actually decreased, but four out of the ten countries still achieved quite significant increases in happiness scores. This hints that there are likely other factors (independent of GDP) such as social support that directly impact happiness.
We also considered the hypothesis that increases in GDP in 2017 could lead to increases in Happiness scores two years later (in 2019). The graph above plots changes in GDP between 2017–2019 with changes in Happiness in 2019–2021. In this case, changes are more proportional than previous graphs. However, only two countries (Canada and India) see consistent direction of change (i.e., other countries see increases in Happiness despite decreases in GDP two years before). This could further reinforce that GDP and Happiness might be independent, and warrants further investigation.
Putting these graphs into context, most countries in the past had exponential growth in their GDP (as the population transitioned from an agriculture-based society to manufacturing), but it could be the case that Happiness does not follow the same exponential growth. Extreme increases in GDP may also jeopardize increases in Happiness by creating negative externalities (e.g., excessive pollution and noise created by manufacturing). Considering third variables also brings in the question of whether subcategories of GDP (such as military investment or consumer spending) have greater impact on happiness scores.
Military Expenditure (As a Share of GDP) and Happiness Scores
Unlike the positive correlations among common country-evaluating metrics, the relationship between a country’s military expenditure as a share of GDP and its Happiness Score had a very slight negative correlation. In fact, according to the scatter plot above, for every GDP percentage increase in military expenditure, a country’s Happiness Score dropped on average by 0.085 points. This unexpected finding displays the importance of taking multiple statistics into account when assessing the well-being of a country. Although a nation may be well prepared to go to war, the well-being of its citizens must also be taken into consideration when determining the prosperity of a country. Statistics regarding how citizens are treated are equally important if not more than those tracking how a country compares to others.
GDP and Gender Equality
Such a statistic is a country’s effort in involving women more in their society. Within recent years, the world as a whole has made immense progress toward gender equality, such as having women as leaders of countries like New Zealand’s prime minister, Jacinda Ardern, and having women, who work as healthcare first responders and social workers, being vital assets in battling the Coronavirus pandemic. Monumental events such as these are representative of a country’s well-being.
To analyze a country’s progression toward gender equality and its relationship to a country’s GDP (Gross Domestic Product), our team analyzed two different datasets from the United Nations Development Programme’s Human Development Reports: the Gender Development Index (GDI) and the Gender Inequality Index (GII).
The GDI measures gender gaps in human development achievements by accounting for disparities between women and men in health, knowledge, and living standards using the same component indicators as in the Human Development Index (HDI). The GDI essentially shows the female HDI as a percentage of the male HDI; thus the higher the GDI, the more gender equality a country has obtained (hdr.undp.org).
As seen on the map above, countries with a darker hue of blue have a higher gender development index, meaning that the disparities between men and women in their respective countries are not as high as countries that have a lighter hue. Countries with a higher GDI tended to be in North America, South America, and Europe. Countries with a lower GDI tended to be countries in Africa, the Middle East, and Southeast Asia.
Compared with GDP, there is a positive correlation between a country’s GDP and its GDI. While the correlation coefficient of 0.097 appeared to be weak, which can be due to the outliers such as the United States and China, when plotting a linear regression line, as shown above, a positive association was found between GDP and GDI. With this, in general, countries with a higher GDP also tended to have a higher GDI.
The GII measures gender inequalities in reproductive health, such as birth rates; empowerment, such as the proportion of parliamentary seats; and economic status, such as labor force participation. The GII was built with an intention to expose differences in the distribution of achievements between women and men; thus, the lower the GII the smaller the disparities between women and men in human development (hdr.undp.org).
In this data visualization, countries with a darker hue of red/orange have a higher GII, indicating that there are more disparities in the proportion of achievements women have achieved, compared to that of men in their respective countries. Similar to GDI, countries with a higher GII tended to be in Africa, the Middle East, Southeast Asia, and South America.
Comparing GII with GDP, when plotting a linear regression line, we can see that there is a negative association between GDP and GII, indicating that countries with a higher GDP tended to have a lower GII, signaling that countries with high GDP, like the United States and China, have made progress in supporting gender equality.
Visually seeing the relationship between GDP, GDI, and GII, we can see that countries that have progressively moved towards achieving gender equality have also tended to be more economically developed.
GDP and the Climate
Climate change and the negative effects humans have had on the environment is another factor that is not properly accounted for in GDP. Things like burning carbon and cutting down forests counts as growth in GDP. In other words, meaning that in some aspects, if a country hurts the environment, their GDP ranking will increase. To look further into the relationship between environmental effects and GDP, we analyzed the CO2 emissions per unit of GDP.
The visualization above depicts how the CO2 intensity of GDP has changed over time for the top ten GDP countries. CO2 intensity is measured by tonnes of CO2 emitted for every unit of GDP produced by a country. The graph shows that CO2 intensity of GDP has generally been decreasing over time. Lower CO2 intensity suggests that GDP will have a lower correlation with environmental degradation. In particular, China’s CO2 intensity has had the most dramatic decrease since 1990 among the countries being observed. But according to the most recent available data, China still has the highest CO2 intensity of these ten countries. While nearly all of these countries have decreasing CO2 intensities, the CO2 intensity of Brazil’s GDP appears to be increasing slightly. Though there are signs that GDP will have a lower correlation with CO2 emissions, there is still a positive correlation between GDP and CO2 emissions, meaning that GDP as a growth metric does not fully account for the impact on the environment.
GDP with Alternative Measures
Over the last decade, the United States, China, and Japan have secured the top 3 spots in the GDP rankings with the following countries generally keeping their same spot. However, when alternative measures such as the happiness scores from the WHR and Green Growth are taken into account, will these rankings change?
For background, Green Growth is a measure used to assess a country’s economic growth and development while also being environmentally sustainable. Green Growth also considers the resources and services we need to maintain our general well-being. This alternative measure accounts for negative environmental effects in a negative manner, unlike GDP, to provide yet another different take on measuring a country’s development.
Note: The table above shows the top ten countries/regions in GDP, GDP per capita, HDI, WHR, and Green Growth.
The stacked bar chart above focuses on data from 2019 and utilizes the top 10 GDP countries. These countries are ranked in terms of the average ranking of GDP, GDP per Capita, HDI, WHR (happiness scores), and Green Growth. Canada takes the first spot — passing the United States — with an average ranking of 19.6. Canada ranked 10th in GDP, but also ranked 9th in the WHR and 16th in the HDI, demonstrating a good performance in both the economy and general well-being. The WHR and HDI measures took a toll on the rankings for countries like China and India, with GDP per Capita further dropping India’s performance all around. On the other hand, China and India performed relatively well in terms of Green Growth, where countries like the United Kingdom performed rather poorly. Overall, the rankings of the top 10 GDP countries were significantly affected when alternative measures with additional factors were incorporated.
Conclusion
Although a significant proportion of this article critiques how GDP is not a good form of measure, it is still generally accurate in terms of economic performance. However, to properly understand the human well-being and development of each country, we should keep alternative measures and factors in mind. Rather than choosing a strict alternative to GDP as many experts have attempted to do, it may be better to consider alternative measures on top of GDP to obtain an accurate measure. As for now, it is important to consider how GDP is not a one-for-all measure and that there are many prominent factors that are not accounted for.
Sources:
https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
https://www.kaggle.com/unsdsn/world-happiness
https://www.kaggle.com/ajaypalsinghlo/world-happiness-report-2021/code
https://stats.oecd.org/Index.aspx?DataSetCode=GREEN_GROWTH
https://hdr.undp.org/en/content/gender-inequality-index-gii
https://hdr.undp.org/en/content/gender-development-index-gdi