If You Think You’re Happy and You Know It?

By Chloe Ching | OIDD245: Data Project 3

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appiness. What is it? When I was younger, it was easy to make me “happy;” something as simple as ice cream before dinner or going to sleep an hour past my bed time would make me absolutely ecstatic. However, as I grew older, I quickly realized that it’s not as easy anymore to achieve this state of “happiness.” As it turns out, I wasn’t the only one constantly yearning for more. Psychologically, humans are known to be chronically unsatisfied. This culture is especially perpetuated in the United States, where most Americans believe that having that one extra thing, such as getting a promotion or buying the newest car, will make their lives that much better. The United States is an example of a country where

“it appears from an outsiders perspective, that the people living in our society are happy in accordance to their wealth. This wealth centered view of happiness is promoted by various media from movies to television to magazines to general advertising indicating that the true American dream is based on one’s wealth and status in society. Acquisition of wealth considered a fundamental part of the American society due to the media’s portrayal of happiness and how this belief is propagated.”

With this American thought process, it can be assumed that more developed countries have happier citizens since they are able to attain higher goals and buy more “things.” If this were the case, Americans would have some of the happiest people — something we know is not true. Thus, by analyzing and testing factors that most Americans think make them happy (education and work, lifestyle, and government contributions) for a variety of countries, we can better understand how Americans can achieve real happiness.

Happiness Factor: Education and Work

Starting with the basics, many Americans believe they will be happier with more money, thus causing them to center their lives around their education and work. People spend decades working away at their jobs simply to “keep up with the Joneses” and achieve what American society defines as successful. Even with this mentality, it is interesting to see that the United States only has the 8th highest average yearly income, as noted on the “Average Yearly Income (2019)” chart.

This is a horizontal bar chart that compares the greatest to least average yearly income for a variety of countries. Although this data is from 2019, it is recent enough to assume the trends are roughly the same.

To break down the previous graph and truly understand how Americans can be happier, I decided to focus primarily on 8 countries: the United States, China, India, Brazil, Nigeria, Russia, Finland, and the United Kingdom. This mix of countries are culturally and geographically diverse, representing most of the continents; Finland was specifically chosen because it is known to be the “happiest country in the world.”

In these stressful jobs, people can lose sight of the “work-life balance” and about 48% of Americans call themselves “workaholics.” Although working longer hours adds to a higher salary, does it contribute to “happiness?” In the “Average Annual Working Hours per Worker (over time until 2017)” chart, it is evident that the United States is roughly in the middle but still greater than the United Kingdom, another similar and known developed country. However, it is clear that China and India have the highest average annual working hours.

This is a scatter plot that compares the average annual working hours per worker in 8 different countries over 17 years. Although this data is from 2017, it is recent enough to assume the trends are roughly the same.

Generally, it is assumed that people with higher education are able to reach the high-paying alluring jobs and thus support themselves and be “happier.” In the “Mean Years Schooling (over time until 2017)” chart, it is clear that the United States and United Kingdom have the highest education levels. This juxtaposition between China and India’s high annual working hours but low mean years of schooling could potentially indicate that most of their working population are working in unskilled labor.

This is a line chart that compares the average number of years of total schooling in 8 different countries over 17 years. Although this data is from 2017, it is recent enough to assume the trends are roughly the same.

Happiness Factor: Lifestyle

Beyond making a living, a person’s lifestyle can greatly impact their “happiness.” Different lifestyle factors, such as anything from eating habits and fitness routines to stress and anxiety, can affect a person’s life expectancy. Americans are notorious for being unhealthy, with less than 3% of Americans actually practicing a “healthy lifestyle.”

In the “Life Expectancy (2019)” chart, Finland, the “happiest country,” has the highest life expectancy, with the United Kingdom and United States close behind. It is interesting to see that the most developed countries have the highest life expectancies, which could be due to various reasons like modern medicine or actual healthier lifestyles.

This is a bar chart that compares the average life expectancy in 8 different countries. Although this data is from 2019, it is recent enough to assume the trends are roughly the same.

Since it is unclear what exactly causes high life expectancy, let’s take a closer look at people’s day-to-day activities. In the “How do people spend their time? (2016)” chart, Americans are relatively consistent with other countries in how they allot their time to different activities. Compared to Finland, the United States and United Kingdom do have slightly higher “paid work” times, which could contribute to the lower life expectancy rates.

This is a stacked bar chart that compares what activities people spend their time on in 8 different countries. Although this data is from 2016, it is recent enough to assume the trends are roughly the same.

Happiness Factor: Government Contributions

Looking outward, an external factor that affects people’s daily lives and general happiness are government contributions. The type of government, such as democratic versus communist, impacts how money is spent on citizens. In the “Government Spending (2011)” chart, most governments spend about 40–50% of their national GDP, except for a couple of African and Asian countries that spend sizably less. Additionally, the United States is in the same spending category as other developed countries, such as the United Kingdom.

This is a choropleth map that compares government spending in a variety of countries around the world. Although this data is from 2011, it is recent enough to assume the trends are roughly the same.

To get a better understanding of how government spending affects citizens, the “Social Expenditure Breakdown (Europe vs. U.S. in 2015)” chart highlights the exact spending actions for Finland, the United Kingdom, and the United States. Since we’re focusing on the United States, I decided to examine the United Kingdom, a comparable country, and Finland, the “happiest country.”

Compared to Finland, the United States spends a significantly smaller percentage of its GDP on social expenditures. Specifically, both the United States and United Kingdom invest significantly less on “Old Age” than Finland does. Additionally, the “U.S. Social Expenditure Breakdown (2015)” chart isolates the United States and shows the country’s emphasis on “Unemployment,” “Survivors,” and “Social Policies.”

[Left] This is a stacked bar chart that compares the percent of GDP spent on social expenditures in Europe versus the U.S. Although this data is from 2015, it is recent enough to assume the trends are roughly the same. [Right] This is a pie chart that shows the breakdown of social expenditures as a percent of GDP for the U.S. Although this data is from 2015, it is recent enough to assume the trends are roughly the same.

Compare: Depressive Disorders

On the other hand, depressive disorders are mood disorders that cause a persistent feeling of sadness and loss of interest,” and essentially the “opposite” of happiness. Thus, theoretically, if the factors that Americans value (higher income, higher levels of education, lower working hours, and higher life expectancy) do contribute to people’s happiness, then there should be a lower rate of depressive disorders in the United States.

A high correlation between any variable and the rate of depressive disorders would indicate that that variable is a factor associated with depression. The correlation in the “Average Yearly Income (2019) vs. Depressive Disorders (2017)” chart is 0.1366129, in the “Mean Years of Schooling (2017) vs. Depressive Disorders (2017)” chart is 0.219022, in the “Average Annual Working Hours per Worker (2017) vs. Depressive Disorders (2017)” chart is -0.4009957, and in the “Life Expectancy (2019) vs. Depressive Disorders (2017)” chart is 0.07189474.

Most of these correlations are too low to justify any relationships; however, the average annual working hours does have a slightly stronger negative correlation with the rate of depressive disorders. This is the opposite of what was initially assumed, but could suggest that people, even Americans, are actually satisfied by their jobs and enjoy working.

[Left] This is a scatter plot that compares the average yearly income with the rate of depressive disorders. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same. [Right] This is a scatter plot that compares the mean years of schooling with the rate of depressive disorders. Although this data is from 2017, it is recent enough to assume the trends are roughly the same.
[Left] This is a scatter plot that compares the average annual working hours per worker with the rate of depressive disorders. Although this data is from 2017, it is recent enough to assume the trends are roughly the same. [Right] This is a scatter plot that compares life expectancy with the rate of depressive disorders. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same.

Compare: Alcohol Use Disorders

Similarly, alcohol use disorders are a “pattern of alcohol use that involves problems controlling your drinking, being preoccupied with alcohol, continuing to use alcohol even when it causes problems, having to drink more to get the same effect, or having withdrawal symptoms when you rapidly decrease or stop drinking,” according to the Mayo Clinic. This can also be used as another indicator that a country is unhappy, which would be seen through a high correlation between any variable and the rate of alcohol use disorders.

The correlation in the “Average Yearly Income (2019) vs. Alcohol Use Disorders (2017)” chart is 0.1457829, in the “Mean Years of Schooling (2017) vs. Alcohol Use Disorders (2017)” chart is 0.5624164, in the “Average Annual Working Hours per Worker (2017) vs. Alcohol Use Disorders (2017)” chart is -0.2038173, and in the “Life Expectancy (2019) vs. Alcohol Use Disorders (2017)” chart is 0.3187373.

Once again, most of these correlations are too low to create any strong associations. It is is notable that mean years of schooling had a higher positive correlation, potentially suggesting that people who are more educated tend to have more drinking issues. Furthermore, average annual working hours was the only variable that had a negative correlation with depressive disorders or alcohol use disorders. This could suggest that working long hours is not as related to depressive disorders and alcohol use disorders as I had originally hypothesized.

[Left] This is a scatter plot that compares average yearly income with the rate of alcohol use disorders. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same. [Right] This is a scatter plot that compares the mean years of schooling with the rate of alcohol use disorders. Although this data is from 2017, it is recent enough to assume the trends are roughly the same.
[Left] This is a scatter plot that compares the average annual working hours per worker with the rate of alcohol use disorders. Although this data is from 2017, it is recent enough to assume the trends are roughly the same. [Right] This is a scatter plot that compares life expectancy with the rate of alcohol use disorders. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same.

From the analysis regarding how the variables relate to depressive disorders and alcohol use disorders, it is evident that these rates generally do not have a high correlation with any of the tangible factors that Americans believe create “happiness.” In other words, there is no guarantee that focusing time and energy on education and work, lifestyle, and government contributions will create happiness.

World Happiness Report

To put everything into perspective, defining “happiness” has been a question that professionals have been struggling with for years. Starting in 2012, the United Nations started the World Happiness Report, a landmark survey of the state of global happiness to celebrate the International Day of Happiness on March 20th. Many of the questions in these reports are subjective and focus on big picture ideas in comparison to the tangible factors that Americans value that were analyzed earlier. Therefore, it will be interesting to analyze if the quantitative variables are associated with the World Happiness Report’s “happiness score,” a formal metric of happiness.

In the “Mean Years of Schooling (2017) vs. Happiness Score (2019)” chart, there is a strong correlation of 0.7871594 and the coefficient is statistically significant compared to an alpha of 0.05. Going back to Americans’ original assumptions, this finding supports the theory that people with higher levels of schooling are generally better-off and therefore think they are happier. Specifically, the more developed countries, such as the United States and the United Kingdom, are on the top right with highest levels of schooling and thus happiness, compared to China and India that are closer to the bottom left. It is interesting to see how Finland and Russia have similar levels of education, but a large difference in happiness.

[Left] This is a linear regression that analyzes the relationship between mean years of schooling and the happiness score. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same. [Right] This is the linear regression output.

Additionally, in the “Average Annual Working Hours per Worker (2017) vs. Happiness Score (2019)” chart, there is a strong negative correlation of -0.8509351 and the coefficient is statistically significant compared to an alpha of 0.01. This reaffirms the original idea about how people who work more would most likely be less happy; however, this differs from the potential suggestions made when annual average working hours was previously analyzed against depressive disorders and alcohol use disorders.

Furthermore, the United States and its “workaholics” are not located on the far right like some may think. In this situation, the more developed countries are actually in the top left with less working hours compared to countries that have higher proportions of unskilled laborers, such as China and India. As expected, Finland has the highest happiness score and the lowest average annual working hours, which is something to think about.

[Left] This is a linear regression that analyzes the relationship between average annual working hours and the happiness score. Although this data is from 2017 and 2019, it is recent enough to assume the trends are roughly the same. [Right] This is the linear regression output.

So … am I happy and I know it?

It’d be nice if I could promise you that this 10 minute article is the secret to life and eternal happiness — but I can’t. However, what I can tell you is that you are one step closer to figuring out what makes YOU happy. Through this analysis, we examined tangible American values that theoretically contributed to happiness (education and work, lifestyle, and government contributions), and found that a couple are loosely related to happiness, but we were not able to conclude anything significant. It’s clear that happiness still isn’t an exact science, but there are other ways to achieve the level of nirvana you had as a child. So, I urge you to take a moment to look inward and think about what genuinely interests and excites you. I’m not talking about doing what you think society expects of you, but instead, try focusing on how Merriam-Webster defines happiness — “a state of well-being and contentment: JOY.”

Data Sources

Tools Used

  • R to analyze the data
  • SelectorGadget to web-scrape
  • Excel to clean the data

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