research topic. This research aims to investigate if happiness depends on the lifestyle inhabitants can allow themselves from a ifnancial point of view. Furthermore, this paper examines in which countries and regions it is possible to have middle-class expenses with average salary. In addition, a correlation between other features and happiness is observed. For specific correlations, linear models and clustering techniques are presented to identify associations. Obtained results and patterns behind them are further discussed which provides a clearer understanding of the happiness, cost of living, financial condition, monthly expenses [2]. However, in 2023, Killingsworth, Kahneman, and Mellers in their article “Income and emotional well-being: conflict resolved”, investigated the relationship between level of income and happiness. For this purpose, a survey was conducted, in which participants were employed adults in the United States, whose yearly income exceeded $10,000. Using quantile regression to analyze trends in lower and higher income ranges (below and above $100,000, respectively), the authors concluded that higher income positively influences respondents' happiness scores. In addition, the authors mentioned that the bottom of happiness distribution rises more rapidly than the top in that range of income. In particular individuals with an annual income below $100,000 experience a 15% faster rise in happiness compared to those earning more. [3] Nevertheless, the role of money is considerably rising in society. We can notice that now there is a trend of “buying happiness”. This century can be described as consumerism and of course, it makes an imprint on people's understanding of happiness. Social media influences it as well, we try to compare our lifestyle with others, it is starting from a small age. Before people did not have that many types of entertainment and could value small things. One of the reasons behind this could be that we are in “endless pursuit of wanting more”[4].
Nowadays term “happiness” is getting more widely used. There are diferent assumptions regarding
the reason behind being happy. For instance, Brooks provides diferent scientific claims regarding the
“ingredients of happiness”. Firstly, happiness is described as subjective well-being, which is comprised
of genes, circumstances, and habits. Habits, according to the author, are comprised of faith, family,
friends, and work. Brooks believes that work in this equation means that the individual can earn success
and serve others. However, he claims, that: “Happiness cannot buy satisfaction.” Since people can never
have enough money, their wants rise with a paycheck, and the picture of a perfect life is adjusted very
quickly [
Kahneman argued in his study from 2010, that income influences life satisfaction, but not happiness
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logistic regressions. Nowadays we can find various indexes of evaluating the life on internet. These
happiness scores are based on diferent features. In this research, we will consider the index provided
by the World Happiness Report [
To study inhabitants’ happiness score and their financial condition, exploratory data analysis was
applied. For this purpose, the quantitative data was used. The data for this research was obtained
from several sources since a dataset containing all required features to perform the desired analysis
was not available publicly. Therefore, WHO[
For this research, we used the following region classification, which includes Australia and Oceania, North America, South America, Central America and Caribbean, Europe, East and Southeast Asia, Central Asia, South Asia, Middle East, and Africa.
To omit bias while using the data from diferent resources, only the common values were used for observation. More details regarding data cleaning will be provided further for the specific datasets.
Every used dataset was available with a .csv extension, was published to the public domain, and is free to use.
The dataset was provided on Kaggle[
The dataset was published as an appendix to figures for World Happiness Report 2023 [
To see if other features impact the life evaluation of people worldwide, we used the following datasets:
Life Expectancy, Human Development Index, Coverage Index for essential health services (UHC index),
number of suicides, which was further adjusted to suicide rate (number of suicides per capita), child
mortality rate, GDP per capita and Net migration. Most of the data was released in 2021, however, the
datasets for child mortality rate and number of suicides were last updated in 2019. We obtained the
datasets for Life expectancy, UHC index, and Child mortality rate from the World Health Organization
[
A final set of features is provided below in Table 1.
Firstly, let us observe the situation regarding cost of living in the world. Using heatmaps, it is clearly seen in which countries inhabitants are left with negative balance after average monthly expenses (from red to light red) and where people have the opportunity to save some money (shades of blue). However, it can be complicated to look at the specific country, therefore, plots of all world parts are provided below. Figure 1.
(a) Europe (b) Africa (d) Latin America (e) North America
(f) Oceania (g) World outlook
Figure 2 shows us the distributional characteristics of groups of savings based on the region classification. We can notice diferent patterns in the world regions based on the shapes and positions of boxplots. Boxplot for Africa is comparatively short, which suggests that country values there, are low dispersed and normally distributed. On average people there cannot aford all expenses. Australia and Oceania show normal distribution and low dispersion as well, however, the average there is a surplus of 3000$. Boxplot for East and Southeast Asia suggests a positively skewed distribution of values, therefore there is higher dispersion in the upper quartile. Nevertheless, on average people in this region are left with almost nothing at the end of the month (median ≈ 0). Let us look at the European region, where the values are also highly dispersed and right skewed distribution can be observed. We can suggest that the reason for this lies in diferent parts of the European region, since in the countries classification we used for research there was no division between countries. There is less dispersion in the 1st and 2nd quartiles of the boxplot, it is interesting that the maximum values go beyond 4000$ (the 4th quartile shows high dispersion of values), however, the median is around 750$. Middle Eastern countries share a similar distribution pattern as European ones, although, the account balance varies there from negative to 1500$. We can notice high dispersion in the 3rd quartile and a significantly low one in the 2nd quartile. Despite a considerably high maximum, on average people in Middle Eastern countries do not have money for savings at the end of the month. Distribution in the Northern American region tends to be normal, therefore, people there, are left with 2000$ in their bank account. South Asian and South American countries share similar patterns; however, we can notice a negative outlier in South Asia. People in both regions do not have enough money to aford the average level of life. Unfortunately, the outlook of the Central Asian region is quite biased, only 2 countries are present in this group due to the low data quality.
Overall, we can observe that the financial situation is quite diferent in world regions, which
demonstrates inequality between countries with developing and developed economies based on the United
Nations M49 standard [
The correlation plot allows us to quantify the direction and strength of the relationship between the included in our research variables. Figure 3 gives us the outlook of correlation in our dataset. We can notice that there is a high positive correlation between happiness ranking and health coverage, human development index, life expectancy, gdp, and financial condition of people. In addition, there high negative correlation between happiness score and child mortality. However, from the plot, we assume that there is a low negative correlation between the suicide rate and other presented variables.
On the other hand, relationships between financial condition and other variables show similar picture as happiness score. The diference is noticeable between life expectancy and financial condition, where the correlation is moderately positive. Finally, we observe a low negative correlation between financial condition and child mortality. It is important to mention that the correlation between inhabitants’ bank accounts and happiness scores is 0.8 in European countries, which is higher than in the world in general. We can suggest that even though in Europe most of the countries are left with a positive balance at the end of the month, people feel happier with higher income. However, the correlation between GDP and happiness rating stays at the same level as in other parts of the world.
The independent t-test was performed to compare the adjusted means of people’s savings in countries
with developing and developed economies [
Overall, we assume that inhabitants in happier countries tend to live longer, are provided with better health services, and can allow more themselves in financial terms.
This subsection aims to find similar patterns in the relationship between happiness scores and the ifnancial condition of people in diferent world regions. For this purpose, a k-means clustering algorithm was used, which is based on computing the distance between points in the dataset and a centroid and assigning it to the cluster. Besides world region clustering (Figure 4a ), European countries clustering is as well presented below (Figure 4b).
For region clustering the optimal number of clusters according to analysis of all indices except GAP, Gamma, Gplus, and Tau, was 3. Therefore, we can observe that two regions, Australia with Oceania and North America have the highest values of happiness score and potential savings of people. In the middle group, we can find countries from Europe, East and Southeast Asia, and Middle East regions. It is worth mentioning that the leaders of the happiest countries list are European countries, however, considering also financial condition of people living there, we can notice that North America and Pacific countries are leading.
The last group is represented by South and Central Asia, Africa, South America, Central America, and the Caribbean. The last three regions mentioned are almost on the same level with East and Southeast Asia by happiness ranking, however, they are showing low results in financial condition.
Let us also look at the clustering plot of European countries 4b. Here we can observe 5 clusters,
however, one of them is represented only by Switzerland. Finland, which is considered the happiest
country in the world [
(a) World Regions (b) European countries
The results of the following linear regression model aim to predict happiness scores based on the amount of money people are left at the end of the month (Figure 5). Looking at the summary closer, we can notice that our model is predicting evenly at both the high and low ends of our dataset since the median value is 0,2. Coeficients allow us to construct the equation for our model. Y = 0.0006680*x + 5.365. As a baseline, if the money at the end of the month equals 0, the happiness score would be 5.365. Then for each 1000$ of saved money, the happiness score of the country would increase by 0.67 points. In our case, the standard error value (0.00007128) is larger than the coeficient, which shows that the coeficient will most likely not be 0. Therefore, the savings coeficient is 9.37 standard errors away from zero, which is quite far to say that the coeficient will not be 0. The P-value is much smaller than 0.05 (<2e-16 and 3.34e-15 in our case), which means that our coeficient is significant for our model. According to residual standard error, our actual happiness scores are 0.83 points away from predicted ones on average, which is quite high taking into consideration our actual highest scores (7.8). Savings at the end of the month explain 47.77% of the variation with happiness score. Looking at the F-Statistic and p-value we can conclude that the null hypothesis is rejected and there is a relationship between happiness score and the amount of money people are left at the end of the month with average expenses.
Now we can look at the provided graphs(Figure 5. Looking at the Normal Q-Q plot we can suggest that the distribution is negatively skewed, which means that our model possibly overestimates happiness score for countries with smaller income and underestimates for countries with higher one. ScaleLocation plot shows us that homoscedasticity is likely to be satisfied for our model. To be sure that homoscedasticity is met, we can perform the Breusch-Pagan test, in our case p-value is 0,175 which is higher than 0,05. Therefore, it indicates that we do not have suficient evidence to claim that heteroscedasticity is present. Based on the Residuals vs Leverage plot, we notice that there are no influential points in our model (all the points stick very closely to Cook’s distance). However, we can remove those points since the model fits the data.
To conclude, taking into consideration all results provided by the linear regression model, there is a relationship between the happiness score of a specific country and the amount of money people are left at the end of the month.
To better understand the relationship between happiness rate and citizens’ financial condition around the world the binary logistic regression was applied. Here we aim to predict if people can cover minimal expenses during the month based on the happiness rate. For this purpose, the threshold of 0 was used for “Savings” The dataset was randomly divided into trainset and test set in proportion 0.7 and 0.3 respectively).
Based on the obtained results we claim that one unit change in happiness rate increases the log odds of people living in country X to have positive balance accounts after necessary expenses by 1,76. The P-value is greater than the significance level, which indicates that the happiness rate is statistically significant.
In addition, binomial logistic regression was used for observing the relationship between happiness rate and citizens’ financial condition around the world. Therefore, we used the happiness score mean as a threshold, which in our case was 5.76. In our original dataset, 43% of sampled countries belong to the group with a happiness rate lower than the mean. The modeling dataset was further randomly divided into a training set, and one used for testing the produced model. Diferent proportions of splitting data were considered to achieve accurate results. Below the results of one split are described, namely 70% for the training set and 30% for testing of the original dataset. According to the obtained p-value, we claim that the savings variable is statistically significant. In our case for every one dollar increase in savings, the log odds of higher happiness rate increases by 0.004. Considering the intercept of the provided model, we assume that the probability of the country where people are left with no money at the end of the month to achieve a happiness rate higher than 5.76 is approximately 49%.
The presented classification model was used to make the predictions on our test data and further evaluated with a confusion matrix. According to statistics of the confusion matrix, there is 89.66% accuracy of the model predictions. The Kappa metric suggests that our model fit is better than random chance (0.79). 0.517 true positive cases were correctly detected by our model compared to 0.55 true positive cases in our dataset. The ROC curve is provided in Figure 6, the curve is close enough to the upper left corner of the graph which indicates the accuracy of our test. The area under ROC curve (AUC) is 0.95.
Based on the described results, we assume that the model fits our data. The financial condition of people impacts the happiness level of the country.
Other models were considered as well, namely the relationship between life expectancy and financial condition and, the prediction of a balance account at the end of the month based on the happiness rate. However, the mentioned models did not show accurate results and, therefore, are not presented in detail.
The applied statistical methods on the used data provide us with a brief overview of the financial condition impact on happiness levels around the world. With other features, such as average life expectancy and human development index we were able to observe how diferent development levels are interrelated with happiness score and cost of living.
One of the biggest limitations of research is that the used data provides a picture only of capital cities, therefore, we cannot make any conclusions regarding the countries overall. On the other hand, it can be considered more as research on capital cities worldwide. We can suggest that in small cities and villages, the situation with expenses and income is diferent from capital. Since most of companies vary the prices for products in diferent countries to ensure a circular flow of economy, but prices inside the country are stable in diferent cities.
Another limitation would be data quality for the African region, since some data provided for the cost of living is absent, as a result, we cannot make fair conclusions regarding this region. In addition, the population could be divided into groups based on age and marital status, since the expenses in families are built diferently from people who live alone. In some families there is only one person who is employed, so in such cases, income is divided between several people.
Since we did not consider the rent expenses in our study, it is dificult to evaluate if the final results are biased. Average property ownership varies from country to country, in some places, it is more afordable to rent the property for living than to own one. We should also consider that not everyone lives in an equal-sized house and there are considerable diferences between countries. Property taxation and conditions for purchasing also lie in governmental processes.
We can suggest that people with higher income buy more, therefore, the demand is higher in such countries. For instance, in some developing countries the individuals’ picture of prosperity would be totally diferent from the ones in developed countries. Several decades ago, people did not have such a variety of products and services available, consequently, their demand was lower. Therefore, further research can be done in observing the level of consumerism and happiness scores worldwide.
From the presented analysis we can see that there is a relationship between the financial condition of inhabitants and happiness rating in diferent countries of the world. However, we also observed the limitations that were faced during the research.
Despite the obtained results, it is quite dificult to say that happiness depends on the level of income. Happiness is something abstract, nobody can describe it. There is a picture of happiness in society, where an individual is free, has a lot of money, and can do whatever s/he wants. In real life, our picture of happiness is based on what we consume in terms of information. Information leads our wants, needs, and therefore our expectations of life. Nevertheless, information is free to use in various parts of the world, news feed stays diferent, and the picture of prosperity, values, satisfaction, happiness, and misery cannot be compared from country to country. Nevertheless, life satisfaction is firstly impacted by the family environment and then by society.