A worldwide review of Life Satisfaction
1. Understanding the Ladder index
In the twentieth century, nations primarily used metrics such as national income and gross domestic product (GDP) to assess public satisfaction. However, it wasn’t until the twenty-first century that governments began acknowledging the importance of happiness as a significant national concern. This new found recognition prompted concerted efforts to address this broader concept through various initiatives. Over time, this collective pursuit evolved into what is now known as the Science of Happiness, focused on quantifying and evaluating nations’ levels of happiness and satisfaction.
Central to this framework is the Cantril Ladder index, which serves as a primary measure of Life Satisfaction. It is typically graded on a scale from 0 to 10, where 10 signifies the highest level of satisfaction.
The accompanying animated graph below depicts the global trajectory of this index from 2005 to 2022. Darker shades of blue signify lower scores on the Ladder scale, indicating correspondingly lower levels of satisfaction, while lighter shades indicate higher levels of satisfaction.
Code preview
world_map <- inner_join(df, world, by = c( "country" = "region"))
year.to.cycle <- sort(unique(df$year))
for (year.to.plot in year.to.cycle) {
plot.to.Save <- world_map %>%
filter(year == year.to.plot) %>%
ggplot(aes(x = long, y = lat)) +
borders("world", colour = "#294564", fill = "#294564") +
geom_polygon(aes(group = group, fill = life.ladder)) +
scale_fill_continuous(name = 'Life Ladder') +
ggtitle(paste("Life Satisfaction in", year.to.plot))
file_path <- file.path("/Users/statistics/Rproject", paste0(year.to.plot, ".png"))
ggsave(plot = plot.to.Save, width = 9, height = 6, filename = file_path, device = "png") }
This collection of plots reveals a discernible positive trend in global life satisfaction over time, characterized by the gradual lightening of the blue hue across all continents. Moreover, certain continents undergo this transition to lighter shades of blue more rapidly. For instance, both the Americas and Europe tend to report heightened levels of satisfaction earlier than other continents. Specifically, North America consistently displays values surpassing 6 points on the ladder scale from 2006. Similarly and despite the fact that South and Central America have different GPD growth indices, their nations generally exhibit a parallel pattern in terms of satisfaction, consistently registering scores surpassing the 6 points from 2008 onward. Europe exemplifies this trend as well. However, certain countries within Europe, such as Portugal, Spain, and Germany, momentarily experience a drop in satisfaction scores to ranges below 4 points, only to swiftly rebound to an overall positive score in no more than a year.
Asia tends to exhibit satisfaction levels that fall within a moderate range for the majority of the time. It is not until 2014 that satisfaction levels in this continent begin to trend towards higher values. Moreover, both Russia and Kazakhstan appear to demonstrate quicker improvements in satisfaction levels compared to other countries like India. Furthermore, it is evident that Africa generally maintains satisfaction levels below 4 points. Nonetheless, there is a positive trend observed for this continent as well, with satisfaction values progressively escalating to higher points, particularly from the year 2009 on-wards.
2. What affects the Ladder index
Outdated beliefs often assert a direct correlation between a nation’s GDP growth and the Ladder Index. However, the preceding analysis occasionally revealed a notable deviation from this assumption. When examining the case of North America and South America as an illustrative example, it became evident that countries within these two continents reported comparable levels of satisfaction, despite considerable differences in their GDP growth rates. Look at the chart below for a more detailed depiction of this scenario.
Code preview
new_df <- subset(df, Continent %in% c("South America", "North America") & year == "2022")
ggplot(new_df, aes(x = log.GDP,
y = reorder(country, life.ladder),
fill = life.ladder)) +
geom_col(position = "dodge") +
geom_text(aes(label = life.ladder),
size = 3,
hjust = -0.5)
The preceding chart Shows the GDP growth rates of all nations in the North and South American continents in 2022; where countries reporting higher scores in the Ladder index are positioned at the top of the chart.
The Dominican Republic presents a remarkable situation, as it displays a GDP growth rate superior to that of many other countries; nevertheless, it holds the lowest Ladder index value (5.51). Conversely, Mexico ranks at the top of the Ladder index, even though countries like Canada or the United States have superior GDP growth rates. These circumstances challenge the notion that satisfaction is solely linked to a nation’s GDP growth. Instead, it implies that Life Satisfaction is a nuanced concept probably influenced by another variable or maybe it’s shaped by the intricate interplay of various socio-economic and cultural factors. The proposition still remains speculative until further investigation is conducted to explore the relationship between the Ladder index with many other factors.
3. The goal of this study
Taking into account the previous hypothesis and recognizing that the Ladder index is just one of several indicators in the Worldwide Happiness Report, this investigation seeks to find a potential correlation between the Ladder index and some of the variables there presented. Please refer to the table below to identify the totally of them. Please notice that the categorical variables Country and Year aren’t indices of happiness, however they are essential to discern the information by place and time.
| Country | Year | Ladder | GDP | Social Support | Life Expectancy | Freedom of Choice | Generosity | Corruption Perception | Positive Affect | Negative Affect |
|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | 2008 | 3.724 | 7.350 | 0.451 | 50.5 | 0.718 | 0.168 | 0.882 | 0.414 | 0.258 |
| Afghanistan | 2009 | 4.402 | 7.509 | 0.552 | 50.8 | 0.679 | 0.191 | 0.850 | 0.481 | 0.237 |
| Afghanistan | 2010 | 4.758 | 7.614 | 0.539 | 51.1 | 0.600 | 0.121 | 0.707 | 0.517 | 0.275 |
| Afghanistan | 2011 | 3.832 | 7.581 | 0.521 | 51.4 | 0.496 | 0.164 | 0.731 | 0.480 | 0.267 |
Among the set of eight indicators, there’s also the GDP growth rate which, as mention before, is traditionally compared to the Ladder index. In contrast, this study will undertake an unconventional comparison of the Ladder index with other variables also found in the Worldwide happiness report. The aim is to determine whether a positive relationship exists. It is important to emphasize that we are not suggesting causation but rather investigating correlation. With this objective in mind, this study will: 1. Conduct a correlation analysis to identify all indicators that exhibit a positive relationship with the Ladder index, and 2. Perform a multiple linear regression analysis to quantify the extent to which each positive related factor influences the Ladder index.
4. Multiple variable correlation
Exploring the relationships between all happiness indicators and the Ladder index can be visually achieved through the construction of multiple scatter plots. Below, there’s an illustration of all possible combinations of variables.
Code preview
df_var <- df[,5:13]
plot <- melt(df_var, id.vars = "life.ladder")
var_labbeler <- c("log.GDP" = "GDP rate",
"social.supp" = "Social Support",
"life.expe" = "Life Expectancy",
"freedom" = "Freedom of Choice",
"Generosity" = "Generosity",
"percept.corruption" = "Corruption Perception",
"positive.affect" = "Positive Affect",
"negative.affect" = "Negative Affect")
ggplot(plot, aes(x = life.ladder, y = value)) +
geom_point(color="steelblue", size = 0.5) +
facet_wrap(~variable, scales = "free", labeller = as_labeller(var_labbeler))
Upon a vague visual examination of the provided charts, it is evident that the variables “GDP rate,” “Social Support,” “Freedom of choice,” “Life expectancy,” and “positive affect” exhibit a positive correlation with the Ladder index. However, for a detailed understanding of this relationship it is essential to utilize quantitative measures of linear dependency such as covariance and correlation coefficients.The first measurement will help to understand the nature of the direction of the linear relationship between all data vectors while the second coefficient will determine how strongly that relationship occurs.
4.1 Covariance
To elaborate further, let’s proceed with the calculation of covariance for the different pairs of variables outlined earlier. It’s important to recall that this value is the measure of change in one variable associated to change in another variable. So, in essence, it measures how two variables fluctuate together. The formula for computing covariance is as follows:
\[ cov(x, y) = \frac{\sum (x_i - \hat{x})(y_i - \hat{y})}{n - 1} \]
In this context, \(n\) represents the sample size, \(x\) is the Ladder index sample and \(y\) is one of the eight explanatory variables. . If the computed covariance between the Ladder index and a specific variable is positive, it suggests a positive association between the two variables.
For additional insight into the R code utilized for computing covariance, click on “Code Preview”.
Code preview
# This is an example of the code applied to find the covariance.
cov(df$life.ladder, df$explanatory_variable, use = "complete.obs")
Bellow you’ll find the result for each explanatory variable:
GDP rate :
## [1] 1.015539Social Support :
## [1] 0.09824236Life Expectancy :
## [1] 5.586066Freedom of choice :
## [1] 0.08462784Generosity :
## [1] 0.0327791Corruption Perception :
## [1] -0.09113025Positive Affect :
## [1] 0.06188387Negative Affect :
## [1] -0.03329363The aftermath reveals that all variables, except “Negative Affect” and “Corruption Perception,” exhibit either a neutral or positive covariance with the Ladder index. Among them, “Life Expectancy” holds the highest magnitude at 5.586, followed by “GDP Rate” with a coefficient of 1.015. Given their positive correlation, it is reasonable to infer that they move in the same direction as the Ladder index. Furthermore, “Social Support,” “Freedom of Choice,” “Generosity,” and “Positive Affect” show covariances close to zero, indicating little to no relation of movement to the Ladder Index.
To elucidate this analysis further, examples illustrating positive, negative, and neutral relationships with the Ladder index are provided bellow:
Code preview
second.plot <- plot[plot$variable %in% c("log.GDP", "Generosity", "negative.affect"),]
var_labbel <- c("log.GDP" = "GDP rate",
"Generosity" = "Generosity",
"negative.affect" = "Negative Affect")
ggplot(second.plot, aes(x = life.ladder, y = value)) +
geom_point(color="steelblue", size = 0.5) +
stat_smooth(method = "lm", col = "red") +
labs(title = "Comparisson of covariance relationships to the Ladder index",
subtitle = "Left: positive relationship. Middle: neutral relationship. Right: negative relationship.") +
facet_wrap(~variable, scales = "free", labeller = as_labeller(var_labbel))Certainly, covariance alone does not provide insight into the strength of the relationship between the explanatory variables and the Ladder index; rather, it illustrates their association with the response variable. Consequently, a pertinent follow-up inquiry is to ascertain the nature of this association, whether it is strong or weak. The correlation formula serves as the tool to uncover this information.
4.2 Correlation
The correlation coefficient is effective in assessing the strength of association among all pairs of vectors, providing insight into the depth of each relationship. It’s crucial to note that this is a standardized measure ranging from -1 to 1, with 1 indicating a strong association and -1 indicating a weak association. The correlation formula is as follows:
\[ corr(x, y) = \frac{\sum (x_i - \hat{x})(y_i - \hat{y})}{ \sqrt{\sum (x_i - \hat{x})^2 \sum (y_i - \hat{y})^2}} \]
where \(x\) is the Ladder index sample and \(y\) is one of the eight explanatory variables. To know more about the code implemented to calculate the correlation in R, please click on Code Preview.
Code preview
# This is just an example
cor(df$life.ladder, df$explanatory_variable, use = "complete.obs")Bellow you’ll find the result for each explanatory variable:
GDP rate :
## [1] 0.7848684Social Support :
## [1] 0.7216624Life Expectancy :
## [1] 0.7134994Freedom of choice :
## [1] 0.5344932Generosity :
## [1] 0.1816298Positive Affect :
## [1] 0.5181695Noticeably, the relationship between the GDP rate and the Ladder index emerges as the strongest among all variables, despite not holding the most positive covariance relationship. Similarly, Life expectancy also exhibits a robust correlation coefficient. It’s likely that in a comparison of covariance and correlation coefficients, this variable would have the strongest positive relationship to the response variable. To enhance clarity and facilitate a better understanding of how variables relate to the Ladder index, a graph juxtaposing the covariance and correlation of all variables is presented below.
Code preview
linear.tools <- data.frame(cov = my_cov_vector[, "life.ladder"],
cor = my_cor_vector[, "life.ladder"])[2:9,]
rownames(linear.tools) <- c("GDP rate",
"Social Support",
"Life Expectancy",
"Freedom of Choice",
"Generosity",
"Corruption Perception",
"Positive Affect",
"Negative Affect")
fig_2 <- linear.tools %>%
plot_ly(x = ~cor,
y = ~cov,
text = row.names(linear.tools),
color = row.names(linear.tools),
colors = 'Blues',
mode = 'markers',
marker = list(size = 20,
opacity = 0.7,
line = list(width = 1, color = '#000000')))
fig_2 %>%
layout( xaxis = list(title = 'Correlation'), yaxis = list(title = 'Covariance'))As anticipated, this multiple variable correlation analysis revealed that Life expectancy exhibits the most robust positive correlation with the Ladder index, surpassing even that of the GDP rate. However, the GDP rate remains the second variable with a notably strong positive correlation to the response variable. Additionally, it was observed that other variables such as Social support, Freedom of Choice, Positive Affect, and Generosity maintain a neutral relationship with the response variable. Finally, Perception of Corruption and Negative Affect demonstrate a weak negative correlation.
5. Multiple linear regression analysis
5.1 Visual introduction
Before constructing a linear regression model, it is valuable to discern the trends of correlation exhibit between Life Expectancy, GDP rate, and the Ladder Index, across different nations over time. If the earlier analysis accurately depicts these relationships, countries in this chart should adhere to a pattern where higher Life Expectancy and GDP rates correspond to higher life satisfaction perceptions (Ladder Index).
The animation below illustrates the trajectory of the explanatory variables, with each dot representing a nation worldwide, and color variations indicating different Ladder scores. You can either click the “Play” button to initiate the automatic animation or manually scroll the round button within the year frame to observe the transition of the plots over time.
Code preview
fig3 <- df %>% plot_ly(x = ~log.GDP,
y = ~life.expe,
size = ~life.ladder,
frame = ~year,
text = ~country,
color = ~life.ladder,
hoverinfo = "text",
type = 'scatter',
mode = 'markers' ) %>%
layout(yaxis = list(range = c(40,79)) ) %>%
animation_opts(200,
transition = 1,
easing = "back",
redraw = FALSE)
fig3
As expected, countries such as Luxembourg, Ireland, and Switzerland, boasting high scores in Life Expectancy and GDP growth, exhibit a more positive perception of life satisfaction, reflected in their high Ladder index scores. Conversely, countries with lower Life Expectancy and GDP growth scores are positioned at the bottom of the Ladder index. However, it’s intriguing to note that the collective relationships depicted in this chart tend to gradually ascend over time, indicating an overall trend for countries to achieve more positive values across all factors.
The next phase in this linear regression analysis entails determining the levels of influence brought by the explanatory variables on the Ladder index. This will involve establishing the linear regression equation in the following chapter.
5.2 The regression model
Since Life Expectancy and GDP have a positive correlation with the Ladder index, they will be included as independent variables in a linear regression model, with the Ladder index serving as the dependent variable. Thus, the model consists of the following components:
Response variable = Ladder Index
First Explanatory Variable = Life Expectancy
Second explanatory Variable = GDP rate.
As the objective is to determine the extent of influence that each explanatory variable exerts on the response variable, a multiple linear regression equation is formulated:
\[ Ladder = b_0 + b_1 \cdot \text{Life Expectancy}+ b_2 \cdot \text{GDP rate} \]
As evident, this equation effectively establishes a relationship
between the Ladder index and the explanatory variables, indicating that
a change in the latter will result in a corresponding change in the
Ladder index. Finally, to implement this equation in R, the command
lm() is utilized.
linear_model <- lm(formula = life.ladder ~ life.expe + log.GDP, data = df)
linear_model##
## Call:
## lm(formula = life.ladder ~ life.expe + log.GDP, data = df)
##
## Coefficients:
## (Intercept) life.expe log.GDP
## -2.34002 0.03339 0.60855Without summarizing the linear model, it’s observable that the y-intercept is -2.3, the first slope is 0.03, and the second slope is 0.6. These are the values visualized into the regression equation:
\[ Ladder = -2.34 + 0.03 \cdot \text{Life Expectancy}+ 0.6 \cdot \text{GDP rate} \]
5.3 Interpretation
The y-intercept \(β_0\) is represents the mean value of the Ladder index in a hypothetical scenario where the independent variables have a value of 0. In simpler terms, it signifies the average value of the dependent variable when \(x = 0\). On the contrary, the first and second slopes \(β_1 , β_1\), denote the expected change in the Ladder index for every one-unit change in Life Expectancy and GDP rate, respectively. As evident, both factors exhibit a positive relationship with the Ladder index. Hence, for each unit increase in Life Expectancy, there is an associated 0.03 unit increase in the Ladder index, and for every unit increase in GDP rate, there is a corresponding 0.6 unit increase in the Ladder index. Notably, the second slope possesses a larger absolute value, indicating a greater variation in the response variable.
Given that the values of the slopes alone do not determine the significance of the linear relationship previously outlined. To assess this, we require more detailed parameters regarding the nature of this model:
summary(linear_model)##
## Call:
## lm(formula = life.ladder ~ life.expe + log.GDP, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4535 -0.4635 -0.0005 0.4995 2.1201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.34002 0.13710 -17.068 <2e-16 ***
## life.expe 0.03339 0.00372 8.978 <2e-16 ***
## log.GDP 0.60855 0.02220 27.408 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6812 on 2131 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.6366, Adjusted R-squared: 0.6363
## F-statistic: 1867 on 2 and 2131 DF, p-value: < 2.2e-16This summary displays the formula and regression coefficients, as
observed before. Additionally, it provides other parameters to evaluate
significance, such as the t-value and the p-value
Pr( > | t | ). These metrics indicate the likelihood of
the model and whether the null hypothesis that asserts no relationship
between the response variable and the explanatory variable, is
valid.
For a two-tailed test, the null and alternative hypotheses are as follows:
- \(H_0 = β_1 = β_2 = 0\) There is no (linear) relationship between the two variables.
- \(H_1 = β_1 ≠ β_2 ≠ 0\) There is a (linear) relationship with the two variables.
Since this p-value of both explanatory variables is is less than 0.05, we can reject the null hypothesis. And assume that there’s a statistically significant relationship between the Ladder index, Life Expectation and GDP rate. Although the regression coefficient for Life Expectation is close to zero, this predictor combined with GDP rate has a significant relationship with the Ladder index.
CHAPTER OF CHOICE:
6. Determinants of Life Evaluations
6.1 Multifaceted ways to happiness
As we navigate the complexities of the modern age, it becomes imperative to unlock the secrets to happiness. In this chapter, we unveil the varying degrees of happiness across different continents and nations.
As we delve deeper into our analysis, we recognize the importance of augmenting our main dataset with additional information. To achieve this, we explore and merge a new dataset that contains complementary data relevant to our analysis. This integration enhances the richness of our data and enables us to perform more nuanced analyses.
With the new dataset, we explore the critical factors that influence life evaluations in 2023 and the creation of the Ladder Score, that derived from a comprehensive assessment that considers various aspects of well-being.
Key factors in this assessment include income, health, social support, freedom to make key life decisions, generosity, and the absence of corruption. We delve into how these factors collectively shape individuals’ perceptions of their well-being and satisfaction with life. Financial stability and prosperity, along with physical and mental health, are fundamental to overall life satisfaction. Additionally, having a reliable support system, autonomy in decision-making, and opportunities for altruism contribute significantly to a sense of purpose and fulfillment. Moreover, a transparent and fair societal framework devoid of corruption fosters trust and confidence, positively impacting life evaluations. By understanding these factors and their interplay, we gain insights into promoting policies and practices that enhance overall quality of life for individuals and communities.
Logged GDP per capita reflects the economic performance of a country, measured by its Gross Domestic Product per person. GPD per capita is measured in terms of Purchasing Power Parity (PPP) and data is sourced from the World Development Indicators (WDI) by the World Bank.
Social support denotes the presence of a reliable support system, such as family or friends, which significantly impacts individuals’ well-being. Social support is calculated as the national average of binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question regarding the availability of relatives or friends to count on during times of trouble.
Healthy life expectancy represents the average number of years a person can expect to live in good health, influencing overall life satisfaction. Time series for healthy life expectancy at birth is constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository. While data is available for select years (2005, 2010, 2015, 2016, and 2019), to align with this report’s sample period (2005-2022), interpolation and extrapolation methods are employed.
Freedom to make life choices refers to the degree of autonomy individuals have to make decisions about their lives, a crucial aspect of well-being. This metric represents the national average of binary responses to the GWP question assessing satisfaction with freedom to choose one’s life path.
Generosity, measured by charitable donations, volunteering, or other acts of kindness, contributes to a sense of purpose and fulfillment.Generosity is measured as the residual of regressing the national average of GWP responses to the donation question on log GDP per capita.
Additionally, perceptions of corruption, indicating the level of perceived corruption in a country, play a significant role in shaping individuals’ trust and satisfaction with societal institutions. Perceptions of corruption are determined by averaging binary responses to two GWP questions regarding the prevalence of corruption in government and businesses. In cases where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.
we also introduce Dystopia, an imaginary country used as a benchmark for comparing other nations. Dystopia represents a society with the world’s lowest levels of happiness, characterized by minimal incomes, life expectancy, generosity, and social support, along with high corruption and limited freedom. By contrasting real-world conditions with those of Dystopia, we gain insights into societal disparities and the importance of promoting global well-being.
6.2 Worldwide Circular Visualization
Plot Components
Stacked Bar: Each segment of the radial plot represents a factor contributing to happiness. The length of the segment corresponds to the value of the factor.
Color Legend: Factors are differentiated by color, making it easy to distinguish between them.
Labels: Country names are displayed along the outer edge of the plot, providing context for each segment.
The findings presented in the radical plot elucidate the average national life evaluations by considering six principal variables: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and freedom from corruption and one extra factor: dystopia. These variables were selected based on their established connections to subjective well-being and, notably, life evaluations, utilizing both experimental and survey data.
2023’s country rankings are derived from life evaluations spanning the years 2020, 2021, and 2022, coinciding with a period characterized by high infection rates and fatalities due to COVID-19.
Finland remains at the top for the sixth consecutive year, with a significant lead over all other countries. Denmark holds second place, while Iceland follows closely in third. Israel climbs to fourth, and the Netherlands, Sweden, Norway, and Switzerland secure the 5th to 8th spots. Luxembourg and New Zealand complete the top ten. Austria, Australia, and Canada maintain strong positions, with Ireland, the United States, Germany, and Belgium also in the top twenty. Lithuania enters the top twenty for the first time, steadily rising over the past six years. While minor differences exist in three-year average scores, significant distinctions are only noticeable between countries with substantial ranking gaps.
6.3 Happiness ring plot by Continent
The countries of Oceania consistently exhibit high ranks for their ladder scores, reflecting a notable prevalence of happiness and life satisfaction across the region. Such consistent high rankings suggest the presence of conducive socio-economic and cultural factors that contribute to the well-being and contentment of individuals residing in these countries. This data not only serves as a testament to Oceania’s commitment to fostering well-being but also provides valuable insights into the potential drivers of happiness within the region.
Meanwhile, Europe emerges as the second leading continent in terms of life ladder rankings, with particular emphasis on the Nordic countries. Renowned for their progressive social policies, robust welfare systems, and high quality of life, the Nordic countries consistently demonstrate exemplary performance in life ladder rankings. Their notable position within Europe’s standings reflects the effectiveness of their societal structures and policies in fostering overall well-being and contentment among their populations.
Similarly, both America and Asia present intriguing patterns in their life ladder rankings. Across the Americas, including North and South America, there is a consistent trend of high scores, indicating widespread happiness and satisfaction among the populace. Likewise, Asia, while historically moderate in its life ladder rankings, has witnessed a gradual shift towards higher values in recent years. These continents’ journeys towards increased life satisfaction highlight evolving socio-economic dynamics and cultural shifts that contribute to enhanced well-being among their diverse populations. As both America and Asia continue to progress, they offer compelling landscapes for further exploration of the factors influencing happiness and life satisfaction on a global scale.
Meanwhile, Africa continues to face challenges, remaining one of the unhappiest continents globally. Despite pockets of progress in some regions, overall life ladder rankings in Africa still indicate lower levels of happiness and life satisfaction. This underscores the ongoing need for targeted interventions and policies aimed at addressing socio-economic disparities and promoting well-being across the continent. As efforts to improve quality of life in Africa persist, there is potential for positive transformations that could elevate life satisfaction levels and contribute to a brighter future for its inhabitants.
7. Conclusion
In conclusion, this report underscores the evolving landscape of how nations measure and understand public satisfaction, transitioning from traditional economic metrics like GDP to a more holistic approach centered around happiness indices. The emergence of the Science of Happiness and the adoption of tools such as the Cantril Ladder index signify a growing recognition among governments worldwide of the importance of well-being as a significant national concern.
The analysis presented in this report reveals a positive trend in global life satisfaction over time, as evidenced by the gradual improvement in Cantril Ladder index scores across all continents. While certain regions exhibit more rapid progress than others, the overall trajectory suggests a collective effort towards enhancing the quality of life and happiness on a global scale.
Moreover, the examination of factors influencing the Cantril Ladder index challenges conventional beliefs regarding the sole correlation between GDP growth and life satisfaction. The findings indicate that satisfaction levels are influenced by a complex interplay of socio-economic and cultural factors, with variables such as life expectancy demonstrating significant positive correlations with the Ladder index.
The subsequent correlation and regression analyses further elucidate these relationships, providing insights into the extent of influence that life expectancy and GDP rate have on life satisfaction, where the first one emerges as the strongest predictor, followed closely by GDP growth rate.
Moving forward, the findings of this study highlight the importance of adopting a multidimensional approach to policymaking, It proves that taking into account not only economic indicators is essential to understand the reality of life satisfaction. By leveraging the insights gained from the juxtaposition all of these indicators, governments can better tailor policies and initiatives to enhance the well-being of their citizens, ultimately fostering more resilient and prosperous societies.
References
The origin of happiness Boris Sardine
https://limn.it/articles/the-origins-of-happiness/
The World Happiness Report 2023