For 30 years, I have been interested in cultural differences. I maintained a database of variables that vary across cultures, starting with Hofestede’s seminal rankings of 40 nations. Finding interesting variables was difficult and time consuming. The world has changed. Today it is easy to find interesting data on happiness, income, or type of government. Statistical software is also free (project R). This has changed the social sciences. Nowadays, the new problem is that data can be analyzed in many ways and that results can be inconclusive. As a result, social scientists can disagree even when the analyze the same data. Here I focus on predictors of national differences in happiness.

Happiness has been defined in many ways and any conclusion about national differences in happiness depends on the definition of happiness. The most widely used definition of happiness in the social sciences is subjective well-being. Accordingly, individuals define for themselves what they consider to be a good life and evaluate how close their actual lives are to their ideal lives. The advantage of this concept of well-being is that it does not impose values on the concept of happiness. Individuals in democratic countries could evaluate their lives based on different criteria than individuals in non-democratic countries. Thus, subjective well-being is not biased in favor of democracy, even though subjective conceptions of happiness emerged along with democracy in Western countries.

The most widely used measure of subjective well-being is Cantril’s ladder. Participants rate their lives on a scale from 0 = worst possible life to 10 = best possible life. This measure leaves it to participants to define what the worst or best possible life it. The best possible life in Denmark could be a very different life than the best possible life in Zimbabwe. Ratings on Cantril’s ladder are imperfect measures of subjective well-being and could distort comparisons of countries, but these ratings are currently used to compare the happiness of over 100 countries (WHR).

The Economist’s Intelligence Unit (EUI) has created ratings of countries’ forms of government that provides a measure of democracy (Democracy Index). Correlating the 2020 happiness means of countries with the democracy index produces a strong (linear) correlation of r = .68 (rank correlation r = .71).

This finding has been used to argue that democracies are better societies because they provide more happiness for their citizens (Williamson, 2022).

So the eastward expansion of democracy isn’t some US-led conspiracy to threaten Russia; it reflects the fact that, when given the choice, citizens tend to choose democracy and hope over autocracy and fear. They know instinctively that it brings a greater chance for happiness.

Although I am more than sympathetic to this argument, I am more doubtful that democracy alone is sufficient to produce more happiness. A strong correlation between democracy and happiness is insufficient to make this argument. It is well known that many predictors of nations’ happiness scores are strongly corelated with each other. One well known predictor is nations’ wealth or purchasing power. Money does buy essential goods. The best predictor of happiness is the median income per person that reflects the spending power of average citizens and is not distorted by international trade or rich elites.

While it is known that purchasing power is a predictor of well-being, it is often ignored how strong the relationship is. The linear correlation across nations is r = .79 (rank r = .82). It is often argued that the relationship between income is not linear and that money is more important in poorer countries. However, the correlation with log income is only slightly higher, r = .83.

This might suggest that purchasing power and democracy are both important for happiness. However, purchasing power and democracy are also strongly correlated, (linear r = .72, rank = .75). Multiple regression analysis can be used to see whether both variables independently contribute to the prediction of happiness.

Of course, dollars cannot be directly compared to ratings on a democracy index. To make the results comparable, I scored both variables from 0 for the lowest possible score to 1 for the highest possible score. For purchasing power, this variable ranged from Madagascar ($398) to Luxembourg ($26,321). For democracy, this variable ranged from Myanmar (1.02) to Norway (9.75).

The results show that purchasing power is a much stronger predictor of happiness than democracy.

The model predicts that a country with the lowest standing on purchasing power and democracy has a score of 3.63 on Cantril’s happiness measure. Increasing wealth to the maximum level without changing democracy would increase happiness to 3.63 + 3.13 = 6.76. In contrast, keeping purchasing power at the lowest level and increasing democracy to the highest level would increase happiness only to 3.63 + 0.48 = 4.11. One problem with statistical analyses across nations is that the sample size is limited by the number of nations. As a result, the positive relationship with democracy is not statistically significant and it is possible that the true effect is zero. In contrast, the effect of purchasing power is highly significant and it is unlikely (less than 5%) that the increase is less than 2.5 points.

Do these results imply that democracy is not very important for citizens’ happiness? Not necessarily. A regression analysis ignores the correlation between the predictor variables. It is possible that the correlation between purchasing power and democracy reflects at least in part a causal effect of democracy on wealth. For example, democratic governments may invest more in education and innovation and achieve higher economic growth. Democracies may also produce better working conditions and policies that benefit the working class rather than wealthy elites.

I will not repeat the mistake of many other social scientists to end with a strong conclusion that fits their world views based on weak and inconclusive data. The main aim of this blog post is to warn readers that social science is much more complicated than the natural sciences. Follow the science makes a lot of sense, when large clinical trials show strong effectiveness of drugs or vaccines. The social sciences can provide valuable information, but do not provide simple rules that can be followed to increase human well-being. This does not mean that social science is irrelevant. Ideally, social scientists would provide factual information and leave the interpretation to educated consumers.