Distributions of well-being

As with people who look at GDP per capita, there is a tendency for people looking at well-being to focus on the means. Doing so, one completely ignores the distribution. To demonstrate this let’s look at two things.

Exercise 11

Choose an indicator (either an overall index, or one of its constituents). First calculate the standard deviation for that indicator for each country. Which countries have particularly high standard deviations – what does that mean about the distribution of well-being there?

Solution

Let’s take self-esteem. The countries with particularly high variation in self-esteem are France, Hungary and Bulgaria. Those with low variation were Slovakia, Sweden and Cyprus. Note that, in this case, there is little relation between the standard deviation for each country and its mean.

Next, let’s look to see how many individuals in each country suffer ‘well-being poverty’, i.e. having a level of well-being below a certain threshold. Starting from the same indicator, recode it so that individuals with a score of -1 or lower are given a code of 1, and other individuals are coded 0 (label 1 as ‘low well-being’). Next use the crosstabs function to see what percentage of individuals in each country have ‘low well-being’ on that indicator.

Solution

Overall, without the combined weight being applied, 11.1% of respondents had ‘low self-esteem’ (defined as having a score below -1). The countries with the highest proportions suffering low self-esteem were Hungary (18.3%), France (18.2%), and Slovakia (17.4%). The countries with the lowest proportions were Cyprus (4.3%), Germany (5.0%) and Switzerland (5.5%). You need to use the recode function first to produce a new binary variable where ‘1’ means having a self-esteem score below -1 and ‘0’ means having one above -1. Then you need to use the crosstabs function with countries and the new binary variable. Make sure you tick the percentages option such that SPSS calculates percentages within countries. If you are struggling to produce these results, see the SPSS syntax below.

Can you think of a way to visually compare the ranking of countries that this method produces, compared to the ranking produced by just taking the mean well-being for each country? Are there any important differences?

Tip

Why not use the ‘rolling hills’ method, ordering the countries in your chart in terms of the mean of the indicator in question, but only plotting the percentage of people with low well-being? To do this you need to go into Excel. If you do this with self-esteem as described above, you’ll get a graph like the following:

SPSS syntax

* Standard deviations.

WEIGHT BY DWEIGHT.
 
MEANS
TABLES=selfesteem BY cntry
/CELLS MEAN COUNT STDDEV.

* Proportions below a certain level.

RECODE
selfesteem (Lowest thru -1=1) (ELSE=0) INTO low_selfesteem.
EXECUTE.
 
WEIGHT BY DWEIGHT.
 
CROSSTABS
/TABLES=cntry BY low_selfesteem
/FORMAT= AVALUE TABLES
/CELLS= COUNT ROW
/COUNT ROUND CELL.
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