I recently came across rankings of countries by average reported happiness produced for The World Happiness Report which was released on March 20, 2021. This is a report produced by independent experts for the United Nations Sustainable Development Solutions Network and uses data from the Gallup World Poll to produce an average happiness score for over 150 countries for each year from 2005 to 2020.
This year’s report focuses on the change in average happiness from 2017-2019 to 2020 to examine the impact of the COVID-19 pandemic on happiness, and to assess the relationship of these changes to the country-level health impacts of COVID-19 and the government responses to it. However, I was more interested in examining the potential relationship of happiness to the latent variable measure I developed for premodern versus modern religious values (see earlier post here).
The main measure used for happiness in the World Happiness Report is based on the national average response to the question on life evaluation in the Gallup World Poll. The English wording of the question is
“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”
This measure is also referred to as the Cantril life ladder. It provides a more stable measure of the satisfaction of people with their lives than the two other measures also included in the Gallup World Poll: positive and negative affect. Positive affect is the degree to which a respondent experienced happiness, laughter and enjoyment yesterday. Negative affect is the degree to which a respondent experienced worry, sadness and anger yesterday.
For this analysis, I averaged the national average life ladder values for the three years 2017 to 2019 for each of 153 countries and territories. I also added modernity and religiosity latent values for 102 of these countries for years 2017-2020 from my previous analysis of the World Values Surveys and the European Values Study. I then did a regression analysis of the association of these latent values and a number of other covariates with the national average happiness, which I used to decompose the contribution of each of these factors to the overall national happiness. I describe that analysis in more detail below, but first I present a graph of the results which also gives the ranking of countries from highest to lowest happiness score.
Finland holds the rank of the happiest country in the world for the third consecutive year, with an average score of 7.81 on the 0-10 scale. It is followed by Denmark, Switzerland, Iceland and Norway. Among the 102 countries included in the chart above, Australia came in at 12th position with a score of 7.22 and the USA in 16th position with a score of 6.94. The countries with the lowest scores on the graph was Rwanda, with Zimbabwe, Yemen and India just above it. Afghanistan received the lowest score, followed by South Sudan, in the overall results for all the countries included in the World Happiness Report.
The World Happiness Report also includes the results of a regression analysis used to attribute the average reported happiness score of each country for years 2018-2020 to its average income, healthy life expectancy and four social factors: social support, freedom to make life choices, generosity and perceptions of corruption. I note that the healthy life expectancy measure used was that I produced for WHO Member States in 2016, when I worked for WHO.
I carried out a similar regression analysis including not only the above explanatory factors, but also the following potential factors:
|modern||latent variable for premodern/modern religious values (see post)|
|religios||latent variable for religiosity (practicing, non-practicing, non-religious, atheist) (see post)|
|hc||human capital: average years of education of persons aged 25+ (International Futures Version 7.31)|
|gfr||global fertility rate (UN Population Division 2017)|
|urban||per cent of population living in urban areas (UN 2018)|
|gini||income inequality index for 2017-2019 from World Bank (2020)|
|stunting||per cent of children aged 0-4 who are stunted (WHO 2017)|
|bmi||body mass index – people aged 40-64 years (Kontis et al 2014)|
|risk||average risk measure for five categories of natural disaster (INFORM)|
|fsi||fragile states index (The Fund for Peace)|
|democ||EIU democracy index (Economist Intelligence Unit)|
|ciri||CIRI Human Rights Index (CIRI Human Rights Data Project)|
|ptsi||Political Terror Scale (Worldwide Governance Indicators – WGI)|
|rle||rule of law effectiveness (WGI)|
|gee||government effectiveness (WGI )|
|pve||political stability and absence of violence (WGI )|
|cce||control of corruption effectiveness (WGI )|
|ymenpct||proportion of the population that are 15-30 year old males |
(UN Population Division 2017)
|hedbal||hedonic balance calculated as positive affect score (GWP) minus negative affect score (GWP)|
|avelf||average ethnolinguistic fractionalization (La Porta 1999)|
|frac_ethn||ethnic fractionalization ( Alesina et al 2003 )|
|frac_lang||linguistic fractionalization ( Alesina et al 2003 )|
|frac_relig||religious fractionalization (Alesina et al 2003)|
The other potential covariates are those that were included in the World Happiness Report analysis:
|lngdp||latent variable for premodern/modern religious values (see post)|
|hale||latent variable for religiosity (practicing, non-practicing, non-religious, atheist) (see post)|
|socsupp||the national average of the binary responses (either 0 or 1) to the GWP question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”|
|freedom||freedom to make life choices is the national average of responses to the GWP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”|
|generos||generosity is the residual of regressing national average of response to the GWP question “Have you donated money to a charity in the past month?” on GDP|
|corrupt||the corruption measure is the national average of the survey responses to two questions in the GWP : “Is corruption widespread throughout the government or not” and “Is corruption widespread within businesses or not?” The overall perception is just the average of the two 0-or-1 responses.|
The following plots provide scatterplots for the happiness index versus lngdp (r = 0.8) and versus modern (r = 0.78).
I started with the regression model used in the World Happiness Report but with the average happiness for 2017-2019, and covariates relating to the same period. This regression explained 81% of the variance across countries in average happiness, though two of the variables, generos and corrupt, had estimated coefficients which were not significant at the 5% level and contributed only very marginally to explained variance. When I added modern to the regression and tested inclusion of other variables, I ended up with the following final model, which explains 84% of overall variance in average happiness across countries:
Note that healthy life expectancy (hale) is no longer included, and that the other new health variables stunting, bmi and risk (disasters) were also not significant explanatory variables. Similarly, the gini measure of income inequality and most of the fractionalization variables were also not retained in the model. Hedonic balance (hedbal) was also unrelated to variations in average happiness on the life ladder. The religious fractionalization variable, frac_relig, was marginally significant (p=0.049) when added to the final model, but this was highly dependent on other variables included. Religious fractionalization reflects the probability that two individuals randomly selected from the population of the country will have different religions. The estimated coefficient for frac_rel was -0.35, so that average happiness decreases with increasing religious fractionalization. I did not include this variable in the decomposition of the contributions of the various factors to the overall happiness of each country shown in the plot above.
The first six sub-bars for each country/territory in the plot above reflect my estimate of the contributions of the variables included in the regression analysis to the average happiness score in that country. The final dark blue bar includes two elements. The first is the residual error, the part of the national average that the model does not explain. The second is the estimated average happiness in a mythical country called dystopia, since its score is the model’s predicted score (1.82) for an imaginary country having the world’s worst observed values for each of the six variables. With dystopia and the residual included, the sum of all the sub-bars adds up to the actual average happiness score on which the rankings are based.
Across all 101 countries on average, GDP per capita and social support each explained around 17-18% of the total happiness score, followed by freedom to make life choices (10%), modern religious values (8%), population proportion of young men (8%) and state repression (7%). Explanatory values vary substantially across individual countries of course.
The proportion of young men (ymenpct) was included as a potential variable because it is an explanatory variable for higher rates of homicide. Based on this association, I expected the model to estimate a negative coefficient for ymenpct, but it was in fact positive, so higher proportions of population being young men are associated with increased happiness. I have no idea whether this reflects any sort of real causal association or not. The association of self-reported happiness with age is generally U-shaped with declining happiness to mid-life and then increasing again. So possibly higher proportions of young men in the population is simply a marker for higher proportions of happier people (male and females).
It is of course not possible to conclude that any of the associations found here are necessarily causal, or that the estimated contributions reflect the real overall contributions if causal pathways could be identified between the various predictors as well as with the outcome happiness measure. However, it seems likely to me that average income per capita, social support, freedom to make life choices, and more modern religious values are indeed all independent causes of increased happiness, although there may well be more complex causal pathways (such as higher income giving more freedom to make life choices, as would more modern religious values) and also that some of these variables may be acting in part as markers for other unmeasured causes of happiness.