Don’t Worry, Be Happy
By Rachel Li, William Huang, Charisse Hung, Aarushi Gupta, Charlotte Huang
Intro
We’ve all heard it before. Live to be happy! Be happy and fulfilled! Find a job that makes you happy and you won’t work a day in your life. It’s as if happiness is the buzzword that just does not die. Yet, what really drives happiness? How can we improve our perception of happiness? How can we… find happiness? Team Happy Pineapples at UCLA DataRes attempts to answer this question by analyzing the World Happiness Report, an annual international study and dataset on the perceptions of happiness, corruption, life expectancy, freedom, and generosity across 153 countries and 10 regions.
Our article is organized according to a few topics which were the center of our research questions. These follow as regional analyses, cultural analyses, and monetary analyses. What did we uncover? Read more to find out!
Who is Happy?
Average Happiness Score Breakdown By Region Per Year
It is commonly known that people have different levels of happiness around the world. Due to their socioeconomic differences, social status, physical characteristics, and much more, they differ on the happiness score they assigned themselves in the World Happiness Report. Thus, we expected there to be a difference in average levels of happiness per country, but we wanted to dive deeper into macro-level trends and trends between years — that is, based on year-to-year changes in geographic regions of the world. The below visualizations all depict the average happiness score breakdown by region per year.
The different colors illustrate sequential years, and the graph is broken down into specific regions to better separate the lines from each other. Clearly, Australia & New Zealand, North America, and Western Europe are the happiest regions of the world, on average, and as measured by their own citizens. An interesting point to note is that Latin America & the Caribbean, a less developed region with scarce wealth, ranked fairly highly in comparison to other regions. Notably, these results fit with our hypotheses and expected trends, where greater developed regions reported higher average happiness scores than less developed regions.
Here, we examined the percentage of Top 30 Happiest countries by region and the changes over time. All countries in Region “Australia and New Zealand” and “North America” were ranked before 30 in year 2015 and 2020. It is also worth noting that in 2015, no country in the regions “Central and Eastern Europe” and “Eastern Asia” were ranked before 30, but several of them were in 2020, indicating development in these regions.
Moreover, we also investigated the average length of the confidence interval of happiness score within each region and changes over time. The confidence interval of happiness score is measured by 1.96*2*standardError(Happiness_Score). The larger the confidence interval, the bigger the difference between happiness scores. Large differences are mainly observed in developed countries (regions). Therefore we hypothesize that the differences in happiness scores is related to the gap of wealth. Moreover, monotonically increasing trends can be observed in Southern Asia and Sub-Saharan Africa, while monotonically decreasing trends in Australia and New Zealand, and Central and Eastern Europe. Combined with the visualization above of happiness across regions over time, some interesting patterns can be analyzed. For Central and Eastern Europe, differences between happiness scores on average decrease over time and the mean happiness scores increase. This indicates that more people are feeling happier as time went by in Central and Eastern Europe. While for Sub-Saharan Africa, though on average people feel happier, the differences between happiness scores are becoming larger. This in fact can be a bad sign,suggesting that wealth is distributed much more unevenly over time, resulting in the larger differences in their happiness.
Happiness across cultures
How else can we categorize countries to explore happiness trends? Most of the countries in the world have a dominant culture that falls under individualism or collectivism. The former emphasizes on individual qualities, and the latter focuses on the interest of a bigger social group. For example, harmony and selflessness, and unity are valued traits in collectivist cultures, whereas independence and personal identity are highly stressed in individualistic cultures. Generally, weastern cultures, such as north american countries and some european countries, tend to have individualistic cultures. Eastern cultures, such as Asian and African countries, value collectivist cultures more. We compared 83 countries in our visualizations based on the categorizations here.
When comparing happiness across different cultures, we had some interesting discoveries. Overall, individualistic countries tend to have a higher happiness score than collectivist countries.
The graph can be reasoned through the difference in ideology, as individualism encourages individuals to pursue their own freedom and happiness.
We also discovered in the dataset that while individualist cultures exhibit higher happiness scores, they perceive the same amount of generosity as collectivist cultures and demonstrate higher perception of corruption than the collectivist cultures.
In This Together
Perhaps we’re…not alone. One might wonder how similar their country’s happiness and other social factors are compared to other countries in the world. In order to investigate this idea, we performed a principal components analysis (PCA) on all relevant data features. A PCA is a type of statistical analysis often used in multivariate data analysis. Without going into the technical details on the algorithm itself, a PCA essentially reduces the number of dimensions in your data by extracting old data features into multiple new independent variables. These independent variables are known as the principal components (PC). In this case, we selected PC1 and PC2 (the first and second axis given by the algorithm) as they reflect the greatest variance in the data.
After conducting the PCA analysis, we color coded each country by region as one might assume socioeconomic differences are minimized in similar regions. Through this analysis, we noticed multiple different clusters of countries that tended to align by region. Of especial interest was Western Europe, where countries split into two different clusterings which may be explained by economic differences between the region. However, other regions like Southeast Asia and North America and ANZ, have large spreads that indicate that each country is relatively unique compared to one another. We also noticed that regions with individualistic/collectivist cultures tended to be clustered near each other. For example, Western Europe and North America and ANZ, regions with a strong emphasis on individualism, were clustered within each other. The more clustered regions also seem to reflect more developed regions of the world. These regions may have a higher overall GDP which can account for better social support, life expectancy, and, as we will see, a lower perception of corruption which may account for these clusterings.
Can Money Buy Happiness?
Luxury car commercials seem to say yes while self-help books disagree. This visualization below shows the relationship between happiness and the median household income of a country.
The income data is represented in units of the international dollar, which is comparable to the US dollar. A logarithmic trendline has been overlaid and matches the distribution of the data. This graph shows that for countries with median household incomes less than roughly $15000, an increase in income leads to a sharp increase in happiness. However, once the median household income of a country is greater than roughly $15000, an increase in income leads to a much smaller increase in happiness.
This makes sense intuitively. The basic needs of Maslow’s hierarchy of needs such as food, water, and shelter can be attained with increased income. Thus if a household lacking in these basic needs gains access to them, this would lead their happiness to increase. Once a household has a comfortable amount of income to sustain their basic needs, however, an increase in earnings may not lead to greater happiness. The remaining needs on Maslow’s hierarchy of needs, psychological and self-fulfillment needs, cannot be explicitly bought with money.
Can Wealth Change the Perception of Corruption in Government?
As we just found, money can buy happiness, although to diminishing returns, but what else can it buy? Is it possible that money can also fundamentally change our perception of corruption within government and businesses? Many times people may assume that a wealthier country has a more well run and trustworthy government and business sector. The graph above shows the relationship between the logged GDP per capita and the perception of corruption (0 being not corrupt at all and 1 being very corrupt) in each distinctive country in 2020.
Countries with a logged GDP per capita below 10 tended to have a perception of corruption within the range of .6–1.0. Countries with a logged GDP per capita above 10, however, had a wide range of perception of corruption values. Many of these said countries are considered international powers (United States, United Kingdom, France, etc) with a strong government and economy. While common beliefs may assume these countries have a well-run government and thus less corruption, many of the more wealthy countries actually have a relatively high perception of corruption compared to their peers. A notable example is the United States of America, where a recent period of political unrest may account for its relatively high perception of corruption. Thus, while most countries with a low perception of corruption have a high logged GDP per capita, the opposite does not hold true.
Don’t worry if you’re not happy though, you’ll live ;)
When analyzing the least happiest 20 countries and top 20 countries with highest suicidal rates, we discovered there’s almost no overlap between any of them. So, being unhappy doesn’t necessarily mean you won’t live.
In the visualization above, pink denotes the 20 least happiest countries. Orange denotes the top 20 counties with the highest suicidal rates.
However, the following visualization shows that happiness score has a positive correlation with the average life expectancy in a country. This is probably because factors such as increased support in physical and social needs can lead to both increased happiness and life expectancy.
Conclusion
Through our exploration of the World Happiness Report, we have looked into who is happy, can money buy happiness, and does happiness affect lifespan. We have found that countries in Australia and New Zealand, North America, and Western Europe have the highest perceived happiness, coinciding with the fact that individualist countries are generally happier than collectivist ones. To answer the age old question, it appears that money can indeed buy happiness, especially when income is low to begin with. The caveat is that at a certain point, increasing income does not lead to a great increase in happiness. We have also found that a low happiness score in a country does not relate to a high suicide rate, however there is a positive correlation between happiness score and life expectancy.
In future studies, it could be worthwhile to analyze other differences between countries and cultures, as well as their correlations with other pieces of data. These may include mortality rate, family size, religion, or any other characteristic that may stand out. Meanwhile, don’t worry, be happy!
If you’re interested in the creation of the visualizations, here is our Github repository. Cheers!
